With the transformation of the real estate industry to digitalization and service, the demand for interdisciplinary composite talents is increasingly urgent. This study focuses on the interdisciplinary housing industry talent cultivation path based on parallel computing technology in the construction of modern industrial colleges in engineering disciplines, and proposes a teaching resource platform architecture based on mobile cloud computing, which integrates Hadoop distributed storage (HDFS), MapReduce parallel computing framework, and secure encryption algorithms (PRE, ABE) to realize the dynamic management of teaching resources and efficient scheduling. At the same time, a student ability assessment model is constructed by combining multi-source data feature extraction and conceptual map analysis, quantifying the importance of knowledge points by using PageRank and HITS algorithms, and dynamically tracking the learning effectiveness through behavioral sequences (LSA residuals, DFT/CWT transformations) and forum sentiment analysis. Comparative analysis through simulation experiments verifies the platform’s significant advantages in resource integration speed, efficiency and sharing performance. With 4000Mbits of resources, the integration speed stabilizes at more than 22Mbits/s, and the traditional platforms NativeXML and WebServices drop to 15.83Mbits/s and 11.95Mbits/s, respectively, and the resource The integration efficiency reaches more than 99%, while the traditional platform is only 97.86% at the highest), and the data loss under the high concurrency scenario (2500 users) is only 5.01KB, while the traditional platform reaches 244.81KB at the highest. The user satisfaction survey reveals that more than 80% of students adopting the Cloud Computing platform are considered to be very satisfied with the practicality of the course content, the ease of use of the resources, and the technical support, which are significantly superior to the traditional platform.
