Investigation on Network Survey Questionnaire Evaluation Based on Neural Network Algorithm

With the development of internet big data analysis technology and the current surge in the number of various types of questionnaire surveys, deep learning based online questionnaire survey analysis is becoming increasingly important. In the construction of the entire online questionnaire survey system, how to improve the accuracy of feedback submitted by respondents and the efficiency of system processing is currently a key issue that needs to be urgently addressed. This article conducts research on online questionnaire surveys and related traditional technologies, constructs an evaluation index system for questionnaire surveys, and combines the application of BP (Back Propagation) neural network algorithm and sample size calculation formula to draw the following conclusion: the questionnaire survey system using BP neural network algorithm on five types of questionnaire survey samples has an average improvement of 6.9% in survey accuracy, and an average improvement of 9.74% in system processing efficiency. This study can achieve personalized analysis and prediction of questioning data from different individuals through personalized model training and prediction, providing more accurate and targeted advice and guidance for individuals.