Recently, Few-shot Knowledge Graph Completion (FKGC) has emerged as a significant research area, yet it encounters challenges stemming from the complexity of multi-semantic relationships in few-shot scenarios. To address these challenges, we propose Task-guided Diffusion Prototype network for FKGC (TDP-FKGC), a method that generates high-quality prototype representations via a task-guided diffusion process. Initially, we analyze the semantics of entity pairs, leveraging attention mechanisms to select pertinent reference pairs from the support set for the creation of a preliminary prototype. Subsequently, a task-guided diffusion process is formulated within this prototype space, and a conditional denoising model is employed to produce task-specific prototype representations. Experimental results demonstrate that TDP-FKGC outperforms current state-of-the-art FKGC methods on three widely used datasets. Furthermore, ablation experiments and analysis of different relationship types confirm the effectiveness and multi-semantic handling ability of our proposed model.