This paper proposes an intelligent management system for personnel resources of medical school teaching platform based on cloud computing, which integrates multi-dimensional personnel data with the architectural features of medical school teaching platform. With the help of Apriori algorithm and improved depth graph clustering model SDCN, personnel association rule mining and personnel resource clustering analysis are realized respectively. Experiments show that the system can effectively recognize four types of personnel groups, and the Apriori algorithm outperforms other algorithms in all performance indexes, with an accuracy rate of 96.28%, a time consuming of 37.65s, a rule coverage rate and a rule reliability of 90.27% and 93.66%, respectively. The results of cluster analysis show that the personnel of associate professors are missing in clusters 1 and 4, and the number of clusters 3 in the personnel of professors is significantly larger than the number of personnel in other positions. The application of the system in this paper helps to reveal the structural problems among positions and provides a technical framework for the intelligent management of medical education resources.
