Human action recognition algorithms have garnered significant research interest due to their vast potential for applications. Existing human behavior recognition algorithms primarily focus on recognizing general behaviors using a large number of datasets. However, in industrial applications, there are typically constraints such as limited sample sizes and high accuracy requirements, necessitating algorithmic improvements. This article proposes a graph convolution neural network model that combines prior knowledge supervision and attention mechanisms, designed to fulfill the specific action recognition requirements for workers installing solar panels. The model extracts prior knowledge from training data, improving the training effectiveness of action recognition models and enhancing the recognition reliability of special actions. The experimental results demonstrate that the method proposed in this paper surpasses traditional models in terms of recognizing solar panel installation actions accurately. The proposed method satisfies the need for highly accurate recognition of designated person behavior in industrial applications, showing promising application prospects.