深度學(xué)習(xí)算法在自然語言處理中的性能優(yōu)化研究
中圖分類號：TP242 文獻(xiàn)標(biāo)志碼：A文章編碼：1672-7274（2025）05-0041-03
Abstract： With the widespread application of deep learning algorithms in the field of naturallanguage procesing （NLP），finding efective performance optimization methods has become a research hotspot.This article first provides an overview of the fundamentalconcepts ofdeep learningand itscore principles inneural networks，andthen explores how deep learning models can respond to specific tasks in natural language processing.Based on data preprocessing and feature engineering，this article analyzes algorithm improvement strategies and training optimization techniques in a targeted manner，and proposes new optimization methods.These methods are mainly aimed at reducing overfitting of the model，accelerating the training process，and improving the model's generalization ability.Trough comparative analysis，theefectiveness ofthe proposed optimization method in dealing with naturallanguage problems was demonstrated，and its performance was comprehensively evaluated.This study not only enriches theapplication of deep learning in natural language processng， but also provides valuable references for future research.
Keywords： deep learning; natural language processing; performance optimization; algorithm improvement training techniques; feature engineering
深度學(xué)習(xí)在自然語言處理（NLP）領(lǐng)域的廣泛應(yīng)用促進(jìn)了算法性能的不斷提升，涉及多種技術(shù)架構(gòu)與優(yōu)化策略。（剩余4780字）