Artificial Intelligence Language Model Architecture Optimization based on Attention Mechanism

With the rapid development of artificial intelligence technology, attention based natural language processing models have made significant progress in improving language comprehension ability. However, existing models still face problems such as high computational complexity, low efficiency in processing long sequences, and unreasonable attention allocation for complex language structures. In response to the above challenges, this article proposes an optimized model architecture that integrates multi-point attention mechanism and locally sensitive hashing technology. By dynamically adjusting the allocation strategy of attention focus and combining the efficient aggregation ability of local sensitive hashing on sparse features, this model reduces the computational complexity of long sequence processing by 42% while maintaining a classification accuracy of over 90%, and significantly improves the semantic parsing accuracy of complex syntax structures. The experimental results show that the optimized model exhibits better performance stability in text scenarios such as financial text analysis and legal document processing, verifying the effectiveness of attention mechanism optimization in improving the practicality of the model. This study provides a new approach to address the efficiency bottleneck of large-scale language models, and its modular design and scalability lay the foundation for cross domain applications.