Driven by mature data mining technologies, the recommendation system has been able to efficiently utilize explicit and implicit information such as score data and behavior traces and then combine the information with complex and advanced deep learning technologies to achieve sound results. Meanwhile, its application requirements also drive the in-depth mining and utilization of basic data and the load reduction of technical requirements to become research hotspots. On this basis, a lightweight recommendation model, namely LG_APIF is proposed, which uses the graph convolutional network (GCN) method to deeply integrate information. According to behavior memory, the model employs Ebbinghaus forgetting curve to simulate the users’ interest change process and adopts linear regression and other relatively lightweight traditional methods to mine adaptive periods and other depth information of items. In addition, it analyzes users’ current interest distribution and calculates the interest value of the item to obtain users’ potential interest type. It further constructs the graph structure of the user-type-item triplet and uses GCN technology after load reduction to generate the final item recommendation list. The experiments have verified the effectiveness of the proposed method. Through the comparison with eight classical models on the datasets of Last.fm, Douban, Yelp, and MovieLens, it is found that the Precision, Recall, and NDCG of the proposed method are improved, with an average improvement of 2.11% on Precision, 1.01% on Recall, and 1.48% on NDCG, respectively.