To solve the problem of high misidentification rate, high missed detection rate, low algorithm execution efficiency, and poor model generalization ability in the recognition of volatile organic compound (VOC) leakage area of infrared gas imager, a VOC leakage area recognition method based on motion feature enhancement was proposed. The video stability threshold was determined by using the statistical method of projection change rate of video sequence, and the moving background and moving foreground were extracted under stable state. Optimized linear stretching was used to perform feature enhancement and outlier filtering on the moving foreground. The motion foreground was fused with the original frame, and VOC leakage area identification was performed using the target detection algorithm. Through the method of model pre-training and transfer learning, the smoke dataset and a small amount of VOC leakage dataset were used to train the recognition model, and the model was transferred to the RK3588S embedded development board for execution efficiency test. Experimental results show that the mean average precision of the proposed algorithm is 0.88 when the intersection over union ratio is 0.5, and the mean average precision is 0.51 when the intersection over union ratio ranges from 0.5 to 0.95. The average recognition time of a single frame is 49 ms, which has high recognition accuracy and recognition efficiency, and can meet the requirements of real-time monitoring. The algorithm in this article can maintain stable model performance and has certain anti-interference capabilities providing some reference for VOC leak identification.