In order to improve the performance of handwritten signature verification algorithm, a handwritten signature verification algorithm based on local binary pattern (LBP) feature and deep learning is presented. Aiming at signature image, preprocessing and Wiener filtering are used to get rid of noise. The preprocessed signature image is divided into 3×4 blocks and LBP is used to each sub-block. The texture histogram characteristics of each sub-block are connected to form a global histogram characteristics. The obtained feature vectors are used as inputs of deep belief network (DBN), grid is trained layer by layer, and the classification plane is formed at the top to recognize the signature image. Experiments are conducted based on GPDS, MCYT and the original database, and the recognition rate errors are 5.85%, 9.3% and 1.17%, respectivly. The handwritten signature recognition accuracy is effectively improved, which meets the requirements of practical application.