This paper proposes an adaptive detection method based on multi-dimensional data analysis for the problem of identifying network security vulnerabilities in digital campuses. A dynamic optimized vulnerability detection framework is constructed by mining the potential features of campus Web logs, combining the unsupervised TF-IDF anomaly load extraction technique with adaptive machine learning model. Among them, the TF-IDF algorithm efficiently screens abnormal requests from 65 million raw logs by quantifying the degree of parameter abnormality. The adaptive model integrates AutoML hyper-parameter optimization and convolutional neural network to achieve dynamic update of detection rules and deep attack pattern extraction. Experimental results show that the method is significantly better than traditional methods in detection efficiency and accuracy: the average detection time is only 54ms, compared to 262ms and 220ms for penetration testing and black-box genetic algorithms, respectively, and the vulnerability detection rate is increased to 100% (0% false positives, 0% misses) and the full vulnerability coverage is completed in the test sites testphp and aisec with a scanning time of 249.57s and 78.13s, respectively, which is much faster than that of traditional methods. The scanning time of testphp and aisec is 249.57s and 78.13s, respectively, to complete the full vulnerability coverage, which is more than 60% higher than the efficiency of AIscanner and WebVulScan tools. The study verifies the dual value of multidimensional data analysis and adaptive modeling in campus network security protection, and provides a feasible solution for realtime vulnerability identification in education informatization scenarios.
