An essential basis for natural language processing is the English corpus. It constitutes an assemblage of numerous English texts that have been painstakingly compiled, structured, and annotated to mirror the real - world utilization of English. Given the issues of sluggish data processing and imprecise alignment features in the design of traditional corpora, this paper presents a Particle Swarm Optimization (PSO) algorithm with the aim of enhancing the efficiency and precision of alignment features in corpus design. By applying this optimization algorithm to feature extraction for constructing an English corpus, particles constantly modify their positions within the solution space and identify the optimal feature extraction approach relying on the individual best solution and the global best solution. Subsequently, a detailed analysis of the target characteristics is carried out. Once the corpus design is finished, the PSO algorithm is employed to thoroughly analyze the alignment features within the corpus, discover vocabulary pairs that are semantically similar or identical in different texts, and compute the word vector similarity of the vocabulary to judge whether they are aligned vocabulary. The language structure and semantic information are also comprehensively analyzed. Ultimately, the experimental outcomes demonstrate that the corpus construction method grounded on the PSO algorithm exhibits substantial enhancements in accuracy, data variety, and translation time, with the translation accuracy ranging from 0.85 to 1.