Abstract: The computing and storage resources of the industrial Internet of things (IIoT) equipment in the coal mine are limited, making it vulnerable to illegal network intrusion, causing sensitive data leakage or malicious tampering, and threatening the safety of coal mine production. Precise recognition of coal mine IIoT equipment can achieve effective management and maintenance of equipment operation, improve equipment safety and protection capabilities. However, existing equipment recognition algorithms suffer from complex feature construction, high memory and computing requirements, making it difficult to deploy in resource limited coal mine IIoT equipment. In order to solve the above problems, a coal mine IIoT equipment recognition model is proposed. Firstly, the model performs traffic segmentation, irrelevant field removal, deduplication, and fixed length field truncation operations on traffic data that supports TCP/IP protocol transmission. The model then converts it to IDX format for storage. Secondly, the model uses convolutional block attention module (CBAM) to optimize depthwise separable convolu-tion(DSC). A lightweight DSC-CBAM model is constructed to filter Non-IIoT equipment. Thirdly, the Wasserstein generative adversarial network with gradient penalty (WGAN-GP) is used to expand the data of coal mine IIoT equipment with less traffic, achieving the goal of balancing offset traffic data. Finally, multi-scale feature fusion (MFF) technology is introduced on the basis of DSC-CBAM to capture shallow global feature information, and Mish activation function is added to improve model training stability. The MFF-DSC-CBAM-Mish (MDCM) model is established to achieve precise recognition of coal mine IIoT equipment. The experimental results show that the model has a fast convergence speed, with accuracy, recall, precision, and F1 score all reaching 99.98%. The model has a small number of parameters, which can accurately and efficiently recognize IIoT equipment in coal mines.