A prediction method for consumer online purchasing behavior based on big data analysis

With the rapid development of electronic networks, consumer online purchasing behavior data presents massive growth and diverse characteristics. How to accurately predict purchasing behavior based on big data analysis becomes the key to improving the quality and efficiency of consumer services. Introducing deep learning methods into purchase prediction research, this paper proposes entity embedding-convolutional neural network-convolutional block attention module (EE-CNN-CBAM) for predicting consumer network purchasing behavior. By entity embedding (EE), high cardinality categorical variables are transformed into low dimensional dense vectors to reduce the computational cost of big data. Using convolutional neural network (CNN) as the core, local association patterns are extracted from user behavior sequences to capture implicit features of consumer online purchasing behavior. And based on the time series data of consumer online purchasing behavior, the characteristic indicators of purchasing behavior patterns are constructed. Convolutional block attention module (CBAM) adjusts channel attention adaptively, allowing the model to prioritize and reinforce the expression of important purchasing behavior features. The experimental results show that EE-CNN-CBAM improves the prediction accuracy on large scale consumer network purchase datasets, providing effective support for consumer behavior prediction in big data environment.