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Aiming at the identification problem of oil pollution of fan equipment that needs to be solved urgently when the oil leakage of fan equipment affects the normal operation of fan equipment, a method of oil pollution detection of fan equipment based on improved deep learning is proposed. Based on the application characteristics of deep learning in object detection, the object detection network YOLOv5n (You Only Look Once v5n) is improved, the non maximum suppression (NMS) in the original network is replaced by Soft-NMS, the false detection rate of the network is reduced, the CA (Coordinate Attention) attention mechanism is added, and the positioning ability of the model to target is enhanced. Improved the original network loss function to the α-IoU (Alpha-Intersection over Union) loss function, improving the accuracy of bounding box detection. Experimental results show that the average accuracy of the model is improved by 8.1%, the totality rate is increased by 19.1%, and the network inference speed is increased by 28.6%. The improved model can accurately detect the oil pollution of the fan, and effectively solve the problem caused by oil leakage in the actual operation of the fan.