This study developed a computer-assisted language learning system based on English syntactic structure using long shortterm memory (LSTM) network. Through dynamic learning models and adaptive learning paths, personalized and efficient learning experiences were provided, aiming to address the shortcomings of traditional systems and improve learners’ mastery of syntactic structure. The system processed language sequence data by designing an LSTM model, combined with a dynamic adjustment mechanism and real-time feedback system, to achieve dynamic adjustment of personalized learning content and difficulty. Based on data-driven teaching strategies, teaching methods were continuously optimized and learning outcomes were improved by collecting and analyzing learning data. The experimental results of this study indicated that the system had significant effects in improving learners’ mastery of syntactic structures, overcoming the limitations of traditional computer-assisted language learning systems. In the experiment, 120 English learners were selected and divided into an experimental group and a control group, using the system developed in this study and the traditional system, respectively. After the experiment, learners in the experimental group significantly improved their mastery of syntactic structures, with their scores jumping from 70 to 94, while those in the control group increased from 68 to 85. The percentage of correct learning increased from 75% to 94% in the experimental group and from 73% to 86% in the control group. The study shows that the computer-assisted language learning system is effective in improving the mastery of syntactic structures.