In the era of big data, the unbalanced distribution of educational resources in colleges and universities leads to significant differences in the quality of education, and the traditional allocation method lacks scientificity and precision. In this study, the improved K-means clustering algorithm is used to conduct multi-dimensional analysis of students’ learning behaviors and educational resources demand, and construct a resource allocation optimization model to achieve intelligent allocation of educational resources. Through clustering analysis of nine learning behavior indicators of 143 college students, the learners were classified into three groups: excellent learners, ordinary learners and risky learners, among which the excellent learners were outstanding in terms of video viewing time (168.43 minutes) and the number of times of chapter study (147.31 times). The correlation between PPT courseware and final test scores was found to be 0.62, indicating that courseware mastery had a significant effect on student performance. Based on this, an optimal allocation model of educational resources containing six indicators, including teacher-student ratio and number of subjects per teacher, was constructed and applied to the allocation of teacher and library resources in five universities. The results show that after allocating 2,000 teachers through the intelligent allocation model, the student-teacher ratio of each university changes from 12.09-19.43 before allocation to 12.09-13.08 after allocation, which realizes the equalization of resource allocation. The research results provide scientific basis for colleges and universities to teach students according to their abilities, and have important guiding value for promoting rational allocation of educational resources and improving teaching quality.