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Clarifying that student portraits can provide personalized learning assistance for students, thereby promoting the development of personalized education. This article presents a new learning method based on K-means. This article collects and preprocesses students' learning behavior, and transforms it into a standardized dataset for users to use directly; in response to the shortcomings of the K-means algorithm in the learning process, this paper adopts a new K-means based learning method, which can effectively improve learning efficiency; on this basis, a new learning strategy was adopted and applied to cluster analysis of student behavior data. On this basis, this article applies the improved FOA (forest optimization algorithm) algorithm to the SVM (Support Vector Mac) classification model. The Gaussian kernel support vector machine classification model can be obtained, and the improved support vector machine has an accuracy of 0.78, a recall rate of 0.88, and an F1 score of 0.84, which is higher than other algorithms. This article helps school education managers better understand students, accurately predict students, and provide personalized services for students.
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