Research on Adaptive Learning Path Optimization Algorithm for Public English Based on Natural Language Processing

In this paper, a learning path optimization algorithm based on natural language processing (NLP) and adaptive learning system is proposed to improve the personalized and intelligent level of public English learning. The algorithm first constructs a semantic understanding model based on word embedding technology (such as Word2Vec), and calculates the semantic similarity between words by mapping them to a high-dimensional vector space, so as to realize the deep parsing of the learning content. Through deep learning methods, especially long short-term memory network (LSTM), the model can effectively capture learners' text input, deeply analyze their language expression and learning state, and accurately identify learners' knowledge points and potential learning disabilities. In terms of learning path optimization, a domain model-based path generation and optimization mechanism is constructed by combining adaptive learning system. By interacting with the learning environment, the system adjusts the decision-making strategy according to the learner's learning progress feedback in real time, and optimizes the learning path in each iteration to ensure the efficiency and accuracy of personalized recommendation. The experimental results show that the proposed algorithm is significantly better than the traditional adaptive learning model in terms of learning effect, path recommendation accuracy and learner engagement, which shows its wide application potential in practical education scenarios.