The influence of high-speed airflow field on the contact pressure and arc state between the pantograph carbon slide plate and the catenary during the operation of high-speed train was analyzed. By calculating the contact pressure and arc state models that are more in line with the actual state, an experimental model of pantograph arc considering the influence of high-speed airflow field is established. In this paper, a 1D-2D-GAF-PCNN-GRU-MSA fault detection model based on the improved black-winged kite algorithm （IBKA）was proposed. The gram-angle field (GAF) was used to convert the one-dimensional contact voltage signal into a two-dimensional image, and the feature recognition was carried out by the parallelizing convolutional neural network (PCNN). In addition, the one-dimensional timing signal is captured by the gated recurrent unit (GRU). The features of the one-dimensional time-series signal and the two-dimensional image are fused to make up for their respective limitations. In view of the parameters in the model, such as the learning rate that is difficult to determine, the number of neurons in the network layer of the gated recurrent unit, and the improved black-winged kite algorithm is integrated to optimize the parameters to make the model more reasonable. Finally, the multi-head self-attention mechanism was fused to improve the accuracy of the model. The proposed model and other three models were tested on three sets of pantograph-net arc models with different experimental conditions, and it was verified that the proposed model had strong robustness and high accuracy.