The corona virus disease 2019 (COVID-19) is severely affects the development of society and economy,and threatens human health.In order to solve the problem that how to identify patients infected with the virus more accurately and quickly,convolutional neutral network (CNN) methods are used to identify COVID-19 chest X-ray images.However,due to the low recognition accuracy of CNN,it is difficult to accurately determine whether a patient is infected with COVID-19.In order to improve the recognition performance of the network for COVID-19 chest X-ray images,firstly,the attention steered trapezoid pyramid fusion network (ASTPNet) is proposed.The ASTPNet can be attached to different CNNs.The characteristics of deep and shallow networks in the model are effectively utilized.Secondly,the attention steered block (AS Block) is proposed to aggregate the weighted information more efficiently to emphasize effective semantic information in channels and spaces,and weaken ineffective interference information through channel and spatial attention.The results show that the accuracy is significantly improved after attaching the ASTPNet to VGG16/19,ResNet34/50 and ResNeXt.When applied to the self-built COVID-19 dataset,and compared with other CNN methods,ASTP-ResNet34 has the better performance.The accuracy reaches 98.40% (two classes) and 97.10% (three classes).It can accurately determine whether the infection of COVID-19.