Recommendation systems provide personalized content suggestions to users based on various approaches. Recommendation systems based on Graph Convolutional Networks (GCNs) are the newest and have yielded promising results. However, most existing GCN-based methods focus only on content or context features, lacking integration of both. This study proposes a new recommendation method using GCN models to learn and combine the content and context features of users and items (called User-Item-Context Graph Convolutional Network), aiming to enhance recommendation quality. The proposed method includes the following main layers: (i) Data representation layer to create initial feature vectors for each entity; (ii) A GCN layer that learns high-level representations for each entity from the initial feature vectors; and (iii) Interaction prediction layer to predict interactions between entities and provide recommendations to users. The proposed method was experimented on MovieLens 100k and MovieLens 1M datasets using common metrics such as Precision, Recall, F1-score, AUC-ROC, MAE and RMSE.