Prediction model of questionnaire survey based on database feature clustering

Linfeng Lv; Nan Li; Jingjing Jiang; Haibo Zhang

doi:10.1117/12.2692599

23 August 2023 Prediction model of questionnaire survey based on database feature clustering

Linfeng Lv, Nan Li, Jingjing Jiang, Haibo Zhang

Author Affiliations +

Proceedings Volume 12784, Second International Conference on Applied Statistics, Computational Mathematics, and Software Engineering (ASCMSE 2023); 1278428 (2023) https://doi.org/10.1117/12.2692599
Event: 2023 2nd International Conference on Applied Statistics, Computational Mathematics and Software Engineering (ASCMSE 2023), 2023, Kaifeng, China

Abstract

The significance of interactive communication lies in the cognitive association and identity gradually formed in social interaction and interaction, which enables the learning process to construct a data feature model and obtain the meaning of intelligent learning. During the implementation of the questionnaire survey project, to test the accuracy of the survey results, considering that the collaborative process can improve the learning and training effect, the links within and between groups in the learning process are constantly improving. As for improving the weak collaborative learning ability of the questionnaire survey, this paper applies the Stacking least squares method to learn and predict in the questionnaire survey. The feature clustering method was also used to improve the accuracy of the model. A clustering classification model based on the integration of basic indicators into the questionnaire content is built to solve the problem of interactive information adoption related to the questionnaire survey platform. A single prediction is transformed into a compound correlation prediction model. Through the analysis of simulation experiments, compared with the traditional mainstream method, it is verified that the prediction accuracy of the deep learning model is significantly improved. The output results can be stable for different combinations of different scales of basic data and deep learning models, which further improves the interactive push ability of the questionnaire survey platform.

Citation Download Citation

Linfeng Lv, Nan Li, Jingjing Jiang, and Haibo Zhang "Prediction model of questionnaire survey based on database feature clustering", Proc. SPIE 12784, Second International Conference on Applied Statistics, Computational Mathematics, and Software Engineering (ASCMSE 2023), 1278428 (23 August 2023); https://doi.org/10.1117/12.2692599

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