Evaluation of English Proficiency Based on Big Data Clustering Algorithm

As one of the common languages in the world, English is playing a more and more important role in trade exchanges, cultural exchanges, and other transnational cooperation. In order to better integrate into the world and communicate with the world, China began to vigorously expand English learning as early as the late 1980s. However, with the development trend of economic globalization and cultural diversity, the traditional English ability evaluation methods are not enough to comprehensively measure individual English ability. Therefore, based on this background, this paper introduces the clustering algorithm into English ability evaluation, improves the traditional clustering algorithm represented by K-means on the basis of predecessors, and captures the evaluation data through neural network algorithm combined with the background of modern big data framework. The results show that the fuzzy logic feature mapping clustering algorithm (FLFM) proposed in this paper performs better than the traditional evaluation algorithm, and the evaluation effectiveness and comprehensiveness are improved by about 10 pt.