Construction and Application of Consumer Behavior Prediction Model Based on Big Data Analysis

This paper aims to solve the problem that traditional market research relies on small-scale samples, and the prediction results may not be accurate enough, resulting in inventory backlogs or out-of-stocks. Through big data analysis, consumers’ purchasing behavior, purchasing preferences, search records, social media interactions, and other data can be collected and analyzed in real-time to avoid excess or shortage of goods. The construction of the model in this paper is divided into model selection and training, model evaluation and optimization, and final deployment and monitoring. Through big data analysis, a large amount of user data information is collected from different channels, and the data is screened and filtered to ensure the accuracy and authenticity of the data, so as to select the corresponding algorithms and models. This paper uses the LSTM (Long Short-Term Memory) prediction model to train the selected data through the training set data, uses the test set to assess the model’s performance, and uses multiple evaluation indicators to evaluate the excellence of a model and whether it can be used online and the performance of the model. The outcomes demonstrate that the performance of the LSTM-based model in data prediction is greater than the traditional deep learning model. In the classification accuracy, precision, recall, and F1 score, this model reaches 92.6%, 93.8%, 91.5%, and 92.6%, respectively, showing good data prediction effect. The LSTM model effectively improves the accuracy and stability of consumer behavior prediction and has strong practical application value. This research provides a new solution for the consumer behavior prediction model of big data analysis, which can provide reliable technical support for data prediction results.