Intelligent Construction and Performance Optimization of Educational Resource Sharing Platform

In order to solve the problems of low resource utilization, slow system response speed, and insufficient resource matching accuracy in current educational resource sharing platforms, the author proposes an intelligent construction and performance optimization scheme for educational resource sharing platforms. The author explores system architecture, resource encapsulation, intelligent scheduling, personalized recommendation, and other aspects, using ant colony particle swarm optimization algorithm to improve resource scheduling efficiency, and combining intelligent recommendation algorithm to optimize learning path planning to enhance user experience. The research results indicate that the maximum number of threads on the platform in the morning is 298, which can accommodate up to 298 concurrent users and meet user needs; The maximum thread count for Platform 1 and Platform 2 is 210 and 240 respectively, which does not meet user requirements, indicating that the platform has good load-bearing capacity. Thus, it is possible to increase the efficiency of education resources, to optimize the reaction time, and to increase the precision of resource matching. Future research will further optimize intelligent recommendation models and resource management mechanisms to promote deep sharing and personalized application of educational resources.