For the problem that the existing multi-source adaptation learning schemes cannot effectively distinguish the useful information in multi-source domains and transfer the information to the target domain, a Multi-source Adaptation Classification Framework with Feature Selection (MACFFS) was proposed. Feature selection and shared feature subspace learning were integrated into a unified framework by MACFFS for joint feature learning. Specifically, multiple source domain classification models were learned and obtained by MACFFS through mapping feature data from multiple source domains into different latent spaces, so as to realize the classification of target domains. Then, the obtained multiple classification results were integrated for the learning of the target domain classification model. In addition, L _2,1 norm sparse regression was used to replace the traditional least squares regression based on L ₂ norm by the framework to improve the robustness. Finally, a variety of existing methods were used to perform experimental comparison and analysis with MACFFS in two tasks. Experimental results show that, compared with the best performing Domain Selection Machine (DSM) in the existing methods, MACFFS has nearly 1/4 of the calculation time saved, and the recognition rate of about 2% improved. In general, with machine learning, statistical learning and other related knowledge combined, MACFFS provides a new idea for the multi-source adaptation method. Furthermore, this method has better performance than the existing methods in recognition applications in real scenes, which had been experimentally proven.