Tactile sensors (flexible electronic skin) play an important role in robot human-computer interaction and tool operation. How to effectively use tactile information for object detection is the main bottleneck of current research. Therefore, a pulse graph convolution neural network, SNN-Atten-ResGCN, is proposed for object detection. Firstly, the graph residual network ResGCN model is used to train the representation of tactile time series. Secondly, the attention mechanism in the deep learning model is introduced to fit the local features of the graphic structure of tactile data. Thirdly, the reconstructed tactile graphics are input, and the SNN pulse neural network composed of three LIF neurons and two FC full connection layers is trained to obtain the feature vector. Finally, vote is utilized to decode the network feature components and discriminate object category. Comparative experiments are carried out on the tactile datasets of EvTouch-Objects and EvTouch-Containers. The experimental results show that the proposed method ensures the model iteration efficiency, and improves the accuracy, precision, recall rate and $F_1$-score of various household objects and container are improved.