At present， in skeleton-based action recognition task， there still are some shortcomings， such as unreasonable data preprocessing， too many model parameters and low recognition accuracy. In order to solve the above problems， a skeleton-based action recognition method based on feature interaction and adaptive fusion， namely AFFGCN（Adaptively Feature Fusion Graph Convolutional Neural Network）， was proposed. Firstly， an adaptive pooling method for data preprocessing to solve the problems of uneven data frame distribution and poor data frame representation was proposed. Secondly， a multi-information feature interaction method was introduced to mine deeper features， so as to improve performance of the model. Finally， an Adaptive Feature Fusion （AFF） module was proposed to fuse graph convolutional features， thereby further improving the model performance. Experimental results show that the proposed method increases 1.2 percentage points compared with baseline method Lightweight Multi-Information Graph Convolutional Neural Network （LMI-GCN） on NTU-RGB+D 60 dataset in both Cross-Subject （CS） and Cross-View （CV） evaluation settings. At the same time， the CS and Cross-Setup （SS） evaluation settings of the proposed method on NTU-RGB+D 120 dataset are increased by 1.5 and 1.4 percentage points respectively compared with those of baseline method LMI-GCN. And the experimental results on single-stream and multi-stream networks show that compared with current mainstream skeleton-based action recognition methods such as Semantics-Guided Neural network （SGN）， the proposed method has less parameters and higher accuracy of the model， showing obvious advantages of the model， and that the model is more suitable for mobile device deployment.