Abstract
In order to extract the features of English distance network education pattern, traditional pattern recognition methods of English distance network education result in low accuracy and large standard deviation of education pattern recognition. This paper proposes a pattern recognition method of English distance network education based on big data algorithm. Using big data technology, the digital English distance teaching resource database is established to avoid the duplication of acquired resources, the random forest algorithm of pattern big data is introduced, the single classifier of decision tree is used to distinguish characteristic data, the number of decision trees in the forest is adjusted, and the self construction process of random forest algorithm is optimized. In the process of pattern recognition, feature level state fusion and support vector machine are used to complete pattern recognition of English distance online education. The experimental results show that compared with the traditional algorithm, the standard deviation of the proposed algorithm is smaller, which can effectively improve the recognition accuracy.
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Duan, Xx., Duan, P. (2021). Pattern Recognition Method of English Distance Online Education Based on Big Data Algorithm. In: Fu, W., Liu, S., Dai, J. (eds) e-Learning, e-Education, and Online Training. eLEOT 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 389. Springer, Cham. https://doi.org/10.1007/978-3-030-84383-0_24
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DOI: https://doi.org/10.1007/978-3-030-84383-0_24
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