International Journal of Computational Linguistics Research Fei Li 16 4 2025 https://doi.org/10.6025/ijclr/2025/16/4/137-145 https://www.dline.info/jcl/fulltext/v16n4/jclv16n4_1.pdf This paper proposes a smart algorithm based teaching model for language education, leveraging a hybrid intelligent algorithm that combines Particle Swarm Optimization (PSO) and Differential Evolution (DE). The model aims to enhance teaching effectiveness by optimizing classroom content and adapting to students’ diverse learning needs. The study identifies 18 effective teaching behaviors through expert consultation and applies the hybrid algorithm to analyze and improve linguistics instruction. Experimental results show the hybrid PSO-DE algorithm outperforms traditional methods like genetic algorithms and ant colony optimiza- tion in convergence speed and solution accuracy. The research highlights that expert teachers’ strategies follow a pyramid shaped effectiveness structure, emphasizing the importance of tailored, data driven in- struction. Findings suggest that integrating adaptive algorithms can significantly boost learning efficiency, student satisfaction, and overall educational quality in language teaching. The authors advocate for broader adoption of such intelligent systems to support teacher development and modernize linguistics education in the digital era.