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Traditional preschool education methods adopt standardized teaching models to solve the problem of neglecting the personalized needs and learning differences of each child in traditional preschool education methods. This article proposed a new algorithm for personalized learning in preschool education that integrated ALS (alternating least squares) and MLP (multilayer perceptron) to improve preschool education and provide personalized education tailored to the needs of preschool children. Firstly, learning data related to preschool children was collected and preprocessed. Then, the children-textbook interaction matrix was constructed, and the alternating least squares method was used for collaborative filtering. Finally, child data, textbook data, and collaborative filtering results were input into a multilayer perceptron for personalized textbook recommendation. The training set data was used for sufficient model training. The experimental results showed that the average accuracy of the ALS-MLP model in textbook Top-4 recommendation reached 95.9%, and recommending personalized textbooks for children could improve their academic performance by an average of 15.6 points. The application of the ALS-MLP model can accurately recommend textbooks based on children’s learning characteristics, providing new methods for personalized learning in preschool education. [ABSTRACT FROM AUTHOR]