With the progress of data acquisition technologies, semi-supervised multi-view learning (SSML) has become a research hotspot in machine learning. Although graph convolutional network (GCN) have garnered significant attention in the domain of SSML due to their unique ability to propagate label signals through graph structures, existing methods still face limitations in feature fusion, which affects the performance of multi-view classification. To address this challenge, we propose a novel framework called Graph-based Consistent Feature Fusion (GCFF). The framework first employs view-specific GCNs to extract features from each view. Subsequently, it fuses these features through an adaptive weighting mechanism and optimizes the fused features using a consistency feature constraint loss, thereby significantly enhancing the quality of the fused features. Finally, a view-common GCN integrates the fused graph structure and consistent features to propagate label signals efficiently, yielding better classification performance. Experiments on four datasets demonstrate the superior performance of GCFF.