Aiming at the fact that the importance of global features is not clear in the text detection of natural scenes, which leads to the misdetection and missed detection of text in the text detection process, a natural scene text detection method based on attention mechanism is proposed. Based on the CTPN network, this method uses the ResNet network and feature fusion technology to extract deeper multi-layer network text features; at the same time, the attention mechanism is introduced into the improved feature extraction network, which is enhanced by the same features gathered from all positions The original features, and the attention weight is obtained, the global attention is collected, and the features that need attention are clarified. Secondly, for the problem of low text positioning accuracy in natural scenes, GIoU (Generalized IoU) loss is used instead of coordinate loss, and the Focal Loss loss function is introduced to improve the original loss function. Experiments show that this method obtains a recall rate of 83%, a precision rate of 87% and an F value of 85% in the text image detection of natural scenes, which ensures the integrity of the text information in the text detection process.