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Traditional hand vein identification is sensitive to rotation, translation, scale, which greatly affects the recognition rate. In this paper, in the case of unrestricted hand posture, a Recurrent Neural Network (RNN) of visual attention model is proposed to deal with the problem of variations of rotation and translation. The model adaptively selects the focus of dorsal hand veins, takes the focus as the center crop the local ROI regions, and sends it into the RNN to train the sequence correlation. The optimization methods in this paper are as follows: the noise of normal distribution is added to select of focus; the number of focus is constrained; the multi-scale local ROI is cropped; the model can be trained using the policy gradient descent method of the reinforcement learning and the optimal cross entropy loss function. The experimental results show that the recognition rate is as high as 99.3%. Compared with the traditional local feature extraction method, the recognition rate of the dorsal hand vein is greatly improved.