The fast growth of digital content in streaming systems has made the problem of too much information more serious. It also brings big challenges to traditional recommendation methods that use experience-based similarity or simple neural network models. We put forward an improved graph convolution framework for personalized movie recommendations to solve three key problems: poor expandability, sparse data, and difficulties in modeling high-level interactions. First, we build a bipartite graph of users and items based on a big dataset of movie ratings. Then we use a simple multi-layer graph convolution method to get high-level collaborative information through standardized neighborhood spread. Different from standard LightGCN models that use inner-product calculation for scoring, our method combines an MLP prediction module with Batch Normalization, non-linear activation functions and Dropout regularization. This design lets us model the interactions between users and items more clearly and keeps the system structure efficient at the same time. The test results from big interaction data sets show the model has steady convergence and good generalization ability. It also gets competitive results in top-K recommendation tests, and there is no obvious overfitting during the training process. We find that mixing simple graph spread with non-linear prediction can improve both the ability to show data features and recommendation precision in big and sparse data environments. This research provides a framework that can expand well for recommendation systems with better structure. It also lays a good base for the future combination of graph learning and semantic model building.