Application of Improved Support Vector Machine Classification Algorithm in Korean Grammar Error Recognition Model

In today's rapid development of high technology, the use of scientific and technological means to make the development of Korean grammar better has made a trend, in order to more adapt to the current development, create intelligent Korean grammar, so that the level of Korean grammar specialization is improved, Korean grammar followed. In Korean grammar, it is very important to collect, analyze, and process the information of the living organism in Korean grammar to achieve the specialization of Korean grammar. Dynamic algorithms cannot accurately collect and analyze information problems in Korean grammar. Therefore, an improved support vector machine classification algorithm is proposed for error identification model analysis. Firstly, computer technology is used to collect Internet information, and the indicators are divided according to the requirements of the error identification model to reduce the interference factors in the error identification model. Then, computer technology forms an error recognition model scheme based on the results of Korean grammar error recognition model, and comprehensively analyzes the error recognition model results. ADAMS simulation shows that the improved support vector machine classification algorithm is better than the dynamic algorithm for the accuracy of the error recognition model and the reliability of the error recognition model of Korean grammar under certain evaluation criteria.