To address the weak discriminative power of Sparse Representation Classification (SRC), a self-adaptive learning algorithm for collaborative representation classification of multi-feature elements named SLMCE_CRC was proposed. Based on the idea of multi-feature sub-dictionary, the sample was collaboratively represented by features and elements, the sparse weights of features and the residual weights of elements were learnd self-adaptively and combined linearly to classify the samples. The experimental results demonstrate the effectiveness and high classification accuracy of the proposed algorithm. It is suitable to images with multi-features.