Title: Content clustering based propagation feature extraction for short video media platforms

Authors: Shiming Zhou

Addresses: Changchun University of Architecture and Civil Engineering, Changchun, Jilin, 130000, China

Abstract: In order to improve the accuracy of short video media platform propagation feature extraction, this paper proposes a content clustering-based propagation feature extraction method for short video media platforms. Firstly, the data obtained through data crawling is user online comments, which are used to obtain user comments on short video media platforms. Secondly, preprocess the propagation data of short video media platforms through data cleaning and text segmentation. Then, input the short video, calculate the classification loss for each frame separately and sum it up. Finally, the content clustering method is used to cluster the propagation features of short video media platforms, and the final propagation features of short video media platforms are obtained by solving the propagation feature function. The experimental results show that the proposed method can effectively improve the accuracy of propagation feature extraction and enhance the recall rate of feature extraction.

Keywords: K-means clustering; content clustering; data crawling; cross entropy loss.

DOI: 10.1504/IJRIS.2025.148715

International Journal of Reasoning-based Intelligent Systems, 2025 Vol.17 No.5, pp.309 - 316

Received: 05 Jun 2023
Accepted: 12 Jul 2023
Published online: 21 Sep 2025 *

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