Abstract: In order to reduce the computational complexity of convolution neural network, improve the over-fitting phenomenon in the process of feature extraction and solve the problem that the classic network model can not effectively deal with large size images, deep feature extraction and classification recognition algorithm based on weighting and dimension reduction was adopted. Based on recognition contribution rate of two features, the results of dimensionality reduction of principal component analysis (PCA) and random projection (RP) method were fused with weighted average, then the results were provided to convolution neural network and the high-level features of image classification were extracted. Euclidean distance classifier was used to classify the recognition objects. After theoretical analysis and experimental verification, the results show that the weight ratio of PCA matrix and RP reduction matrix is 6:4, and the recognition rate is over 96% after the preprocess of data by weighting and dimension reduction. This algorithm improves the accuracy effectively, makes large size pictures having good recognition effect in deep learning network and improves the adaptability of network.