Aiming at the problem that weakly supervised action localization method cannot locate action directly and the localization accuracy is not high， a weakly supervised action localization method based on attention mechanism was proposed， and an action localization model based on the pre-frame and post-frame information of action frame and the distinguishing function was designed and realized. The attention value generation model of Conditional Variational AutoEncoder （CVAE） was used to generate frame-level attention values as pseudo-frame-level labels； which CAVE was improved to obtain the frame-level attention value by adding the pre-frame and post-frame information of the action frame； to train and optimize pseudo-frame-level labels repeatedly， the optimization model for attention value based on distinguishing function was used. The experimental results conducted on THUMOS14 and ActivityNet1.2 datasets show that the action localization model based on the pre- and post-frame information of the action frame and the distinguishing function has better action localization effect and accuracy， which missing detection rate reduced by 11.7% compared with the model without the pre-frame and post-frame information of action frame； compared with AutoLoc， Weakly-supervised Temporal Activity Localization and Classification framework （W-TALC）， 3C-Net and other weakly supervised action localization models， when Intersection over Union （IoU） value is set to 0.5， the mean Average Precision （mAP） value on THUMOS14 dataset is improved by more than 10.7%， and the mAP value on ActivityNet1.2 dataset is improved by more than 8.8%.