The effectiveness of recommendation systems heavily relies on accurately predicting user ratings for items based on user preferences and item attributes derived from ratings and reviews. However, the increasing presence of fake user data in these ratings and reviews poses significant challenges, hindering feature extraction, diminishing rating prediction accuracy, and eroding user trust in the system. To tackle this issue, we propose a robust rating prediction model for recommendation systems that integrates fake user detection and multi-layer feature fusion. Our model utilizes a GraphSAGE-based submodel to filter out fake user data from rating data and review texts. To strengthen fake user detection, we enhance GraphSAGE by selecting aggregation neighbors based on the collusion fraud degree among users, and employ an attention mechanism to weigh the contribution of each neighbor during representation aggregation. Furthermore, we introduce a multi-layer feature fusion submodel to integrate diverse features extracted from the filtered ratings and reviews. For deep feature extraction from review texts, we implement a temporal attention mechanism to analyze the relevance of reviews over time. For shallow feature extraction from rating data, we incorporate trust evaluation mechanism and cloud model to assess the influence of trusted neighbors’ ratings. In our evaluation, we compare our model against six baseline models for fake user detection and four rating prediction models across five datasets. The results demonstrate that our model exhibits significant performance advantages in both fake user detection and rating prediction.