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If hydraulic oil leaks from the gap between the blades and the cylinder during the operation of the hydraulic swing cylinder, it will lead to a decrease in pressure, reduce output torque, and fail to achieve the expected operating effect. Therefore, a machine learning based prediction model for the sealing performance of hydraulic swing cylinder blades is proposed. Establish a blade sealing performance index system using sensitivity and fatigue indicators, collect sealing performance sample data, calculate sealing leakage rate and sealing ring heat, and use Reynolds equation to calculate the coupling relationship between oil film thickness and sealing gap oil film pressure; Based on the training data of BP artificial neural network in the machine learning method, the continuous S-type function is converted into the neuron activation function, and the global error is lower than the set minimum value, then the sealing performance prediction results can be obtained. The experimental results show that the built model has high prediction accuracy and fast efficiency for the sealing performance of hydraulic swing cylinder blades, providing reliable reference for mechanical design work in related fields.