This study focuses on the design and optimization of consumer behavior prediction models in online e-commerce reviews. To address the issues of slow convergence and insufficient robustness in the traditional Q-learning reinforcement learning algorithm, this article introduces a probabilistic action-selection algorithm. This algorithm employs a multi-step, iterative mechanism that uses instantaneous differencing to increase the likelihood of selecting high-Q actions during model iteration, thereby accelerating the solution process and ensuring robust network optimization. Given the nonlinear and high-noise characteristics of consumer behavior time-series data in e-commerce reviews, we propose a hybrid intelligent prediction model, Q-learning-Artificial Neural Network-Hidden Markov Model (QL-ANN-HMM), that effectively reduces the impact of systematic random errors and significantly improves prediction accuracy. Experimental results demonstrate that the improved Q-learning algorithm achieves 2.71% and 5.96% improvements in mean absolute percentage error (MAPE) and normalized mean squared error (NMSE), respectively, compared to the traditional Q-learning algorithm on the Amazon Reviews 2023 and Flipkart Reviews datasets. Additionally, the QL-ANN-HMM model achieves lower mean absolute error (MAE), MAPE, and NMSE values on both datasets, recorded at 0.0195, 0.019, and 0.0189, respectively. This research not only provides novel theoretical support and technical methods for predicting consumer behavior in online e-commerce reviews but also enables e-commerce platforms to more accurately track market dynamics, optimize resource allocation, and achieve sustainable development by comprehensively analyzing consumer behavioral data.
Lin et al. (Mon,) studied this question.
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