Deep learning algorithms

with their ability to extract features end-to-end

have established the primary direction of technological development in the field of natural language processing. This paper primarily investigates the application of these algorithms in engineering contexts and analyses the semantic representation dimensions of multi-layer neural networks. To address obstacles such as high data dependency

low model transparency

and high computational costs

an improved approach combining transfer learning with knowledge-guided learning is proposed. Analysis indicates that integrating model compression with explicit knowledge can significantly enhance the algorithm's adaptability in vertical domains. This paper provides a theoretical framework for developing intelligent text processing systems to improve the robustness and execution efficiency of algorithms in complex business scenarios.