Static hand gesture recognition based on multi-feature weighted fusion is proposed to solve the problems of singularity and omission in convolutional neural network for feature extraction. Firstly, the Fourier and Hu moments of the segmented gesture image are extracted and fused as the local features. Besides, a dual-channel convolutional neural network is designed to extract the deep features of the gesture image, which are further treated by dimensionality reduction by principal component analysis. Secondly, the extracted local and deep features are weighted and fused as effective description for hand gesture recognition. Finally, gesture images are classified with Softmax classifier. Experimental results verify the proposed method, and the recognition accuracy reaches over 99% on the image dataset.