K-hop Shortest Path Query (KSPQ) is a fundamental graph operation that aims to find the shortest path between two nodes within K hops. As graph data scales, owners outsource storage and computation to cloud servers, using encryption and pruning to ensure privacy and efficiency. However, integrating privacy protection and graph pruning in encrypted environments remains unexplored, and existing methods struggle to balance security and query efficiency. To address this, we propose a Graph Pruning-Based Privacy-Preserving K-hop Shortest Path Query (PP-KSPQ) method, emphasizing three core features: 1. Graph Preprocessing: Simplifies the graph by removing edges and nodes that violate constraints, reducing the search space by up to 75%; 2. Accelerated Indexing: Employs the middle node cut technique to efficiently identify candidates and speed up path construction; 3. Encryption Integration: Advanced encryption techniques and Secure Integer Comparison Protocols are employed to enable efficient path queries while preserving privacy. Experiments validate that PP-KSPQ enhances both privacy protection and computational efficiency, demonstrating its effectiveness across real-world datasets.