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In the process of learning English, the status of spoken language is particularly important, and it is also the most concerned aspect of most English learners. However, the current situation is that due to the limited resources of traditional teachers and the lack of oral practice environment, it is difficult for many learners to effectively improve their English level. Based on this, this study builds a smart English recognition system based on support vector machine. Moreover, this paper introduces a support vector machine to characterize speech signals. In addition, this paper uses feature fusion to map complex nonlinear relationships between features based on support vector machines and establishes a smart English recognition system based on support vector machine. The model can accurately identify the syllables and pronunciations in the words. Moreover, the use of a large-scale corpus based on non-specific people in this article can represent the generality of spoken learner. [ABSTRACT FROM AUTHOR]