With the proliferation of intelligent algorithms and online media ecosystems, college students are increasingly exposed to complex digital environments that influence their behavioral and psychological profiles. This study proposes an optimization model for student assessment systems in educational management, grounded in data fusion algorithms and structural equation modeling (SEM). Drawing upon a multi-dimensional theoretical framework, the study investigates how family experience, school environment, stress factors, and intelligent algorithmic recommendation contribute to students’ exposure to negative media content and, subsequently, to online behavioral misconduct. Data were collected from 372 college students using a validated Likert-scale questionnaire covering six latent variables and 18 measurement items. SEM results demonstrate that negative media content significantly mediates the impact of school experience, stressors, and algorithmic influence on behavioral misconduct, while family experience shows no statistically significant direct effect. Furthermore, a supplementary machine learning analysis using a Random Forest classifier revealed an F1-score of 0.83 and AUC of 0.89, highlighting the predictive power of fused variables such as algorithmic feedback and psychological stress in identifying students at risk.
In higher vocational education, English courses are always running through them. English ability is an important part of higher vocational education. Effective English teaching is very important to improve students’ English ability in vocational colleges. In this context, based on the ARCS model, this manuscript has designed a high-quality college English teaching optimization and talent training model, with a view to solving the problems of students’ low interest in grammar learning, and then through the model parameter selection and strategy optimization, improve the impact of professional English training, so that inefficient teaching can be transformed into efficient teaching. The experimental results show that the average score of the experimental layer is much higher than the average score of the experimental layer, and the experimental layer has made great progress. The accuracy of the model exceeds 93.89%. The T test of the model has T values of 8.461, 3.421 and 5.230 respectively in English class recognition, attitude towards teachers’ questions, and English class learning attitude, and the numerical value is lower than 0.01, indicating that the ideal bow pattern can effectively improve the students’ forward-looking language and professional enthusiasm.
This study proposes an advanced English Translation Scoring System (ETSS) designed to improve the reliability and accuracy of English translation evaluation using an enhanced BP neural network and an improved Generalized Maximum Probability Ratio Detection (GLR) algorithm. The system features a comprehensive three-phase approach involving text feature extraction, optimization, and interactive fusion to analyze and score translations effectively. Key enhancements include the integration of a wavelet packet decomposition method for feature vector analysis and the use of context-aware corrections to address structural ambiguities. Evaluation of the system demonstrates a significant improvement in translation judgment accuracy, reducing human intervention and error rates. The ETSS system achieves over 95% accuracy in identity detection and an overall system reliability of 92.3%, validating its effectiveness as an intelligent tool for automated English translation assessment.
In the context of accelerating the digital transformation of classical texts, this study explores the semantic system of spatial verbs in Zuo Zhuan through a cognitive linguistic lens and bioinformatic modeling. By leveraging the SikuBERT pre-trained language model, which is specifically fine-tuned for classical Chinese processing, this paper constructs a digital knowledge base of spatial verbs extracted from the Zuo Zhuan corpus. Through a multi-step framework involving automatic semantic annotation, part-of-speech tagging, similarity-based clustering, and vector encoding, the study identifies four core types of spatial verbs: motion, state, existence, and direction. Each verb type is further analyzed in relation to three quantitative dimensions: temporal-quantitative relations, behavioral-quantitative relations, and scene-component relations. The results reveal that motion verbs exhibit the highest semantic frequency and spatial-temporal diversity, while direction verbs are less frequent and predominantly metaphorical. Furthermore, the study introduces biomechanical concepts such as displacement, velocity, and dynamic trajectory to deepen the interpretative framework for motion verbs, thereby bridging linguistic representation with ancient depictions of human physical behavior. Evaluation metrics (Precision, Recall, and F1-score) indicate high accuracy in spatial verb classification using SikuBERT, confirming its effectiveness in ancient Chinese NLP tasks.
This study investigates the biomechanical mechanisms underlying the adaptive evolution of residents’ consumption structures in response to the development of digital finance in China. Drawing on panel data from 31 provinces between 2012 and 2022, the analysis integrates concepts from behavioral biomechanics to interpret how digital finance enhances individual adaptability in rapidly changing technological environments. Empirical results from fixed effects and instrumental variable regressions reveal that digital finance significantly promotes consumption upgrading, shifting expenditure from survival to developmental and hedonic categories. This adaptive shift is mediated by improved income levels, expanded access to consumer credit, enhanced payment environments, and increased entrepreneurial and innovation activities. Heterogeneity analysis indicates stronger effects in high-income regions and economically developed eastern provinces. The study proposes that digital finance functions as a technological ecosystem that fosters behavioral flexibility, innovation, and resilience— paralleling adaptive mechanisms in biological systems.
As multi-track music creation jobs become more complicated, it has become more difficult to capture the interdependencies between tracks and produce high-quality, music theory-compliant music. In order to address the issues of co-generation and music theory restrictions in multi-track music production, we provide in this study a novel framework, M2S-GAN, based on Generative Adversarial Networks (GAN) and Transformer. In order to achieve collaborative multi-track generation, we present the Cross-Track Attention method, which uses cross-track self-attentive learning to capture the intricate relationships and long-term dependencies between various tracks. To guarantee that the produced music satisfies the standards of conventional music theory in terms of harmony, melody, and rhythm, the model also integrates music theory rules to direct the production process in the form of mathematical models. M2S-GAN improves the diversity and caliber of the generated music in addition to increasing generation stability through the skillful architecture of several generating and discriminative networks. According to experimental results, the suggested model performs better than current approaches in terms of the generated multitrack music’s quality, stability, and musical rationality. It can also continue to produce outstanding results across a variety of datasets and assessment criteria. Our work offers a fresh perspective on multi-track music generating and robust support for automated music generation and creation.
With the increasing demand for precision and personalization in physical education, biomechanics has emerged as a pivotal discipline in enhancing the quality of sports dance instruction. This study investigates the integration of biomechanical analysis and intelligent mobile terminals into sports dance pedagogy. It first proposes a system architecture that leverages wearable devices and multimedia teaching terminals to enable real-time feedback, kinematic analysis, and personalized instructional support. Based on national curriculum standards and pedagogical theory, a hierarchical indicator system comprising 3 primary, 10 secondary, and 32 tertiary teaching strategy indicators was constructed and validated using expert consultation and Kendall’s concordance test (W > 0.75). A weighting system was then applied to rank the importance of each indicator. A case study involving 25 physical dance instructors across six universities was conducted to evaluate current teaching practices and assess the applicability of the proposed strategy framework. Results indicate that most instructors rely heavily on traditional methods such as demonstration and verbal instruction, with limited use of multimedia or theoretical integration.
Timely and effective interactive feedback between teachers and students in classroom teaching is of great significance for the improvement of students’ learning motivation, classroom efficiency and teaching quality. This paper designs a smart classroom interactive teaching system based on ZigBee technology. And with the help of smart classroom interactive analysis coding system to test the application effect of interactive teaching system in business English classroom. After testing, the system in this paper can effectively improve the interactive flexibility of the smart classroom and shorten the time to open the system interface compared with the Flanders interactive analysis system based on event records and the classroom interactive teaching system based on 5G wireless communication, which can provide a guarantee for the learning and teaching efficiency of students and teachers. Taking the teaching of business English course as an example to test the system, the system in this paper helps to stimulate students’ interest in learning, promote students’ active learning, improve students’ classroom participation, and enhance students’ performance in business English. The study combined with students’ classroom performance data proves that the interactive teaching system based on communication technology can improve the effect of teacher-student interaction and feedback in the business English classroom.
Since the traditional deep neural network cannot provide accurate serialized recommendation in the process of high-dimensional data collaborative filtering recommendation, thus this paper adds the attention mechanism to the traditional deep neural network to optimize the model structure, and proposes the data recommendation algorithm based on the improved deep neural network. Combined with the index performance comparison between the improved algorithm and the classical serialized recommendation algorithm, the accuracy of the improved deep neural network in high-dimensional data recommendation is verified. In order to realize the multifaceted evaluation of piano playing, pitch, duration, fingering, depth, and muscle stiffness evaluation indexes are proposed. Utilizing the computational volume advantage of the deep separable convolutional algorithm and equipped with the SE Block Attention Module, the multidimensional data generated during piano playing is analyzed. The combined recognition rate of piano playing gestures (down-finger, through-finger, across-finger, expanding and retracting) by the neural network model utilizing the deep separable convolutional network architecture + SE Block attention mechanism is 93.54%, which meets the requirements of the piano playing evaluation system.
The application of machine learning in the field of music teaching is emerging, especially in piano teaching shows obvious potential. In this paper, we propose a key touch action recognition model based on ADAG-SVM. The Gaussian kernel function is chosen to solve the problem of high-dimensional vectors in the input space and feature space of action representation. And the optimal penalty parameters as well as the kernel radius are obtained to further improve the performance of touch-key action recognition. In addition, the piano timbre feature matrix is extracted, and based on the discrete Fourier transform, a tone synthesis model with editable timbre is established to generate expressive demonstration clips to deepen the students’ understanding of tonal expressiveness. The keystroke action recognition model in this paper can predict the change of the angle of the keystroke action of the student’s fingers, and provide scientific action guidance and correction for the learners. The LSD and MSD values of piano tones generated by this paper’s algorithm are 1.52 and 492.68 respectively, which are lower than those of the comparison algorithm. At the end of teaching, the scores of the C2 class under the intervention of this paper’s machine learning method in the piano level evaluation dimension improved by 0.62-1.97 points compared with the traditional teaching C1 class. Students’ satisfaction with the piano teaching class under this paper’s method ranged from 4.35 to 4.81 points, and the overall satisfaction reached 4.59.The piano teaching method combined with machine learning significantly improved students’ keystroke skills and tonal expression.