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Due to the high requirements for the deep camera environment in complex scenes, wearable devices are not natural, and the lack of data set samples based on the deep learning model leads to poor recognition ability and robustness, A gesture recognition method based on deep learning model based on semantic segmentation and neural network based on transfer learning is proposed. By rotating and flipping the collected image data set at different angles, data set samples were enhanced, segmentation model was trained to segment gesture areas, and gesture feature vectors were extracted better through transfer learning convolutional neural network. Softmax function is used for gesture classification and recognition. Through 10 gestures made by 4 people in different backgrounds, the experimental results show that they can correctly recognize gestures in complex environments.