File fragment classification is crucial in digital forensics, aiding in the recovery and reconstruction of fragmented files, which serve as key evidence; while deep learning techniques have advanced in this area, challenges remain, particularly regarding the consideration of inter-file-type relationships and the granularity of classification. To overcome these challenges, we introduce a hierarchical classification approach that leverages an agglomerative hierarchical clustering algorithm combined with a dynamic adjustment mechanism, optimizing category distribution among leaf nodes. This structure is further enhanced by developing specific classifiers for each leaf node, tailored to its unique characteristics. Experimental results on the FFT-75 dataset show that our method achieves 76.3% accuracy in a 75-class scenario (512-byte blocks), surpassing the accuracy achieved with existing approaches. This method improves classification accuracy, addressing misclassification issues caused by excessive classification types.