Development of Intelligent Learning Model Based on Ant Colony Optimization Algorithm

In the process of the gradual popularization of online courses, learners are increasingly dissatisfied with the recommendation mechanism of imprecise courses when faced with a large number of course choices. How to better recommend relevant courses to targeted users has become a current research hotspot. An intelligent learning model based on ant colony optimization algorithm is introduced, which can accurately calculate the similarity between courses and learners. After structured classification, the model recommends courses to learners in the optimal way. The results showed that the accuracy of this method reached 10-20 when tested in Sphere and Ellipse functions, and the optimal solution for problem Ulysses21 was 27, which was better than Advanced Sorting Ant System (ASrank), Maximum Minimum Ant System (MMAS), and Ant System (AS) based on optimization sorting. The proposed ant colony optimization algorithm had better convergence performance than ASrank, MMAS, and AS algorithms, with a shortest path of 53.5. After reaching Root Mean Square Error (RMSE) and Relative Deviation (RD) distributions of 6% and 8%, the stability of the proposed method no longer decreased with increasing RMSE. The accuracy did not vary significantly with changes in the dataset, and the reproducibility performance was better than other comparison models. In the scenarios of path Block and path Naive, the proposed algorithm had an average computation time of only 1011, which was better than the Ant Colony Optimization (ACO) and Massive Multilingual Speech (MMS) models. Therefore, the proposed algorithm improves the performance of intelligent learning models, solves the problem of local optima while enhancing the convergence efficiency of the model, and provides new solutions and directions for increasing the recommendation performance of online learning platforms. © (2024), (Science and Information Organization). All rights reserved.