To address the limitations of using a singular criterion and the reduced reliability observed in the sole application of target detection networks for resolving conflicts among service vehicles on aprons, an apron service vehicle behavior detection network, termed STPA-SlowFast, was proposed. The network incorporated a spatio-temporal pyramid attention module, which was designed to enhance the extraction of spatio-temporal features across multiple scale channels while preserving the structural integrity of these features. This enabled subtle behavioral variations in the operations of service vehicles to be more effectively captured. To mitigate the issue of unbalanced category sample proportions in existing datasets, the network's loss function was refined with Focal Loss, which adjusted the weighting across different categories. This improvement reduced the distortion in detection accuracy caused by sparse training samples in certain categories. Through experimental validation and analysis, STPA-SlowFast was shown to achieve superior performance compared to existing mainstream spatio-temporal behavior detection networks. The proposed network attained an average detection accuracy of 81.46% for operational behaviors of apron service vehicles. Notably, for low-sample categories involving special behaviors, such as turning, the accuracy is increased by approximately 23.94%. These improvements significantly contribute to enhancing the operational safety of apron service vehicles.