Conventional graph neural network models have limited processing power when handling large-scale graphs and are unable to represent intricate interactions between nodes. They have trouble effectively removing representative subgraphs from such massive graphs, which lowers their precision in both inference and training. This paper proposes a rumor detection model, the Multi-Feature Fusion Rumor Detection Model (MFLAN), which is built on an upgraded graph attention network. First, MFLAN uses a feature fusion approach with an attention mechanism, giving various weights to each feature before performing a weighted sum operation on the original features to produce a fused feature vector. Second, positive positional encoding is added so that the model can obtain a representation of the positional information. Then, a learnable parameter matrix is introduced, which allows the model to automatically learn and optimize parameter values during training. Finally, attention scores are sparsified, with certain irrelevant nodes in the large-scale graph receiving zero attention, resulting in the MFLAN model's attention sparsity. The experimental results show that the MFLAN model obtained accuracy rates of 97.71% on Ma-Weibo and 97.10% on Weibo23, reflecting improvements of 1.07% and 1.12%, respectively, over the Dir-GNN model. Furthermore, the MFLAN model outperformed other rumor detection algorithms across a variety of measures in this investigation.