Nowadays, researchers are paying special attention to the classification algorithms related to facial expression, and improving the accuracy of classification is of practical value to frontier fields such as artificial intelligence. The classic methods for image classification are linear discriminant analysis and sparse representation. This study proposes an improved collaborative representation algorithm, aiming at the high computational complexity of image classification, feature utilization, and classification accuracy. First, the block weighted local binary patterns are applied to the texture feature vector of each sub-block. Then, principal component analysis is used to avoid the curse of dimensionality and also increase the running speed of the proposed algorithm. Finally, a collaborative-competitive representation algorithm is adopted to obtain the final classification results. In conclusion, the combination of feature extraction with collaborative representation algorithms has a good classification effect.