In view of the timeliness and accuracy of traditional state recognition algorithms, this paper proposes an improvement measure for foreground segmentation and target recognition. Foreground segmentation is to model the background information in the scene before recognizing the image, to separate the foreground target from the scene, at the same time to reduce the impact of noise, shadow and other environmental changes as much as possible, and then segment the target through a sliding window strategy. Target recognition is improved with reference to the PRN network and anchorboxes mechanism, and a more advanced clustering k-means++ algorithm is applied. The method has low error rate, high signal-to-noise ratio and fast processing speed. Finally, the proposed improved algorithm is applied to device status recognition, which shows the advanced nature of the algorithm.