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Gesture recognition in engineering applications requires high real-time and accuracy, and the on-site environment usually cannot provide sufficient computing power. Using lightweight neural networks can solve the above problems while achieving recognition equivalent to deep neural networks Effect. To this end, a gesture recognition method based on an improved lightweight neural network is proposed. This method improves the ReXNet network structure for hand key point detection to improve the local attention of bone points; at the same time, the key point detection loss function MSE is replaced by Huber loss to improve the anti-interference of outliers. After the experimental environment is built based on the ordinary monocular lens to capture the image, the YOLO v3 hand recognition model and the improved ReXNet key point detection model are used to define different gestures according to the vector angles that constrain the key points of the hand bones, and finally achieve real-time detection. Effect. The test results of the improved model on the RWTH public data set show that the detection accuracy of the improved gesture recognition method is 2.62% higher than that before the improvement, reaching 96.18%, and the convergence speed is faster.