This paper addresses the challenges of high computational cost and severe parameter redundancy in the fine-tuning of large language models. It proposes an efficient fine-tuning algorithm that integrates structural pruning with parameter sharing. The method operates from both the architectural and optimization perspectives. It prunes redundant connections dynamically while keeping the core model frozen and introduces task-conditioned cross-layer sharing modules to enhance representation power and parameter efficiency. A pruning residual compensation mechanism is designed to preserve semantic coherence, and a conditional sharing mapping is constructed to improve task-level consistency. The training objective jointly optimizes task loss, sparsity regularization, and inter-layer consistency constraints, achieving unified parameter compression and semantic retention. The proposed method is systematically evaluated using perplexity, accuracy, and inference speed-up across different pruning rates, learning rates, input lengths, and data distribution settings. Experimental results show that the algorithm consistently outperforms mainstream fine-tuning techniques across multiple dimensions. It achieves joint optimization of accuracy and efficiency with minimal parameter tuning, making it well-suited for large language model deployment and transfer learning across diverse scenarios.