To address the limitations of existing video anomaly detection methods in fine-grained anomaly event prediction, a CLIP-based multimodal feature fusion method for fine-grained weakly supervised video anomaly detection, named ClipFusionVAD, was proposed. First, features from video frames and text were extracted separately using the pre-trained CLIP model. A feature aggregator and a feature projector were designed to achieve multi-dimensional feature fusion, enhancing the representation capability of multimodal features and enabling accurate fine-grained anomaly event classification. Furthermore, GAM (global attention module), which combines channel and spatial attention mechanisms, was introduced to improve the discriminative power of visual features. Experimental results demonstrate that ClipFusionVAD achieves outstanding performance on fine-grained anomaly detection tasks, with mAP attaining 9.4% on the UCF-Crime dataset and 28.07% on the XD-Violence dataset, which further verifies the effectiveness of the proposed method.