Model compression can significantly improve the deployment of convolutional neural networks on limited-resource devices. Filter pruning has gradually drawn attention from academia and industry as a research hotspot. The essence of filter pruning is the selection and retention of important filters. Existing research has primarily focused on static and interlayer filter selection, which still has redundancy in the compressed model. We propose an adaptive dynamic filter pruning approach in this paper wherein an activation weight generation module is introduced to generate the activation weight of each filter. The importance of filters in global convolutional layers is dynamically evaluated by embedding the activation weight generation module in various classical networks, and the filters that extract richer information are adaptively selected to reconstruct the pruned networks. Experiments are performed on CIFAR-10 and AUC datasets using different convolutional neural networks, among which the proposed method has better performance than several mainstream pruning methods on CIFAR-10 dataset. The accuracy decreased by 0.3 percentage points when the computation was compressed by ~70% before and after pruning on the AUC dataset. Experiments on various networks demonstrate the proposed method's ability to generalize to different models.