Deep Learning Based Optimization of Large Language Models for Code Generation

In order to improve the performance of the code generation system in semantic modeling and structural dependency construction, a deep learning-based multi-layer Transformer encoding and decoding structure is constructed, and the overall architecture consists of 12 layers of Transformer modules stacked together, with a multi-head self-attention mechanism (8-head attention) and a positional feed-forward network (dimension 2048) to enhance the contextual modeling capability. The encoder input sequence is capped at 512, fusing positional embeddings with semantic embeddings generated by Item2Vec to achieve accurate capture of variable dependencies and syntactic levels. The decoder introduces multi-tasking goals to jointly perform code completion and semantic annotation tasks to improve generalization and context adaptation. The platform combines neural collaborative filtering structure and multimodal semantic fusion strategy, which significantly enhances the comprehensive understanding of user behavioral features and code structure graph. It reaches 47.82, 53.67 and 28.94 in BLEU-4, CodeBLEU and Exact Match, respectively, and the inference response latency is optimized from 257ms to 87ms, showing excellent accuracy and response efficiency.