Classification Method of Teaching Resources Based on Improved KNN Algorithm

Yingbo An, Meiling Xu, Chen Shen


In order to effectively utilize the network teaching resources, a teaching resource classification method based on the improved KNN (K-Nearest Neighbor) algorithm was proposed. Taking the text class primary and secondary school teaching resources as the research object, combined with the domain characteristics, the KNN algorithm was improved. By measuring the sample space density, the text of the high-density area was found. Different clipping methods were proposed for both intra-class and inter-class regions. The problem of cropping in the space of multiple class boundaries was considered. Results showed that the method ensured uniform distribution of samples and reduced the time of classification. Therefore, under the Weka platform, the improved KNN algorithm is effective.


text classification; KNN; primary and secondary school teaching resources; sample cutting

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Copyright (c) 2019 Yingbo An, Meiling Xu, Chen Shen

International Journal of Emerging Technologies in Learning (iJET) – eISSN: 1863-0383
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