In this paper, an innovative solution is proposed to address the problems of single content design, lack of relevance and specialized training in traditional accounting laboratory courses. In interdisciplinary cooperation, an efficient intelligent assistance model for accounting laboratory courses is constructed by combining the collaborative filtering recommendation model and K-Means clustering algorithm. The model significantly improves the recommendation accuracy and computational efficiency by optimizing the cosine similarity calculation and introducing the similarity-weighted average, combined with the enhanced dichotomous K-means algorithm. After dataset testing and practical application verification in a university, the model effectively improves the quality of personalized recommendation of teaching resources. In particular, students’ mastery of knowledge points K2 and K3 is more adequate through the model recommendation, while their mastery of K8 is significantly improved. In addition, the model successfully identifies the similarity between students, which lays the foundation for providing more personalized learning resources. Overall, the model solves the traditional problem of accounting laboratory courses and provides a practical methodology for intelligent recommendation of teaching resources in universities.