Analysis of Syntactic Complexity and Semantic Coherence of Academic EnglishWriting Based on Particle SwarmOptimization

Writing is an important part of testing language ability, and it is urgent to „nd some objective indicators to determine and evaluate the surface language structure, which will help language learners’ better master the target language. Complexity and semantic coherence are considered to be an important factor in the teaching of second language writing. In practice, due to the complexity of English writing syntax, such as a large number of high-dimensional nonlinear optimization problems, a new intelligent evaluation method is needed to solve them. At present, particle swarm optimization (PSO) has been widely used in function optimization, neural network training, combinatorial optimization, and other „elds. is paper studies the syntactic complexity and semantic coherence of academic English writing based on PSO. e number of phrases is related to writing achievement. When the number of experiments reaches 25, the signi„cant values of syntactic complexity and semantic coherence of data mining algorithm, arti„cial intelligence algorithm, decision tree algorithm, and PSO algorithm are 0.008, 0.003, 0.002, and 0.013, respectively, which shows that PSO algorithm is the best among them.