The invention provides a method for predicting a membrane protein beta-barrel transmembrane area based on sparse coding and chain training and relates to a sparse coding technology, a chain learning algorithm and a support vector machine. Structure prediction is conducted on the membrane protein beta-barrel transmembrane area through a computing method, and important information is provided for the research of the structures and functions of proteins. According to the method, the concept of digital image processing is introduced creatively, sparse coding is conducted on a protein feature matrix, and feature dimensionality reduction and denoising are achieved; a membrane protein beta-barrel data set is organized in a protein database PDB, a position-specific scoring matrix and a Z score are extracted and used as features, the position-specific scoring matrix represents amino acid evolution information, the Z score represents the position information of amino acid residues, a feature vector is extracted through a sliding window, multi-feature fusion is achieved, a chain learning algorithm training model based on a SVM classifier is provided, a predication effect is remarkably improved, and a Jakenife cross validation result shows that the precision can reach 92.5%.