Construction of an Intelligent E-Commerce Big Data Analysis System Based on Deep Learning

Background: In the context of rapid development of e-commerce, how to maximise the potential of big data to optimise user interactions and improve service quality has become a key point of research. This study proposes a concept of an intelligent e-commerce big data analytics system based on deep learning, which deeply mines e-commerce data covering multiple dimensions such as user behavioural trajectories, product details, and transaction history, aiming to achieve fine-grained product recommendation algorithms, customized marketing solutions, and optimized inventory control processes. Method: The system relies on a distributed architecture to enhance its processing performance on massive datasets and ensure that it can provide immediate analytical feedback. We employ a variety of deep learning techniques, including Convolutional Neural Networks (CNNs) for image recognition, Recurrent Neural Networks (RNNs) and their variant Long Short-Term Memory Networks (LSTMs) for sequential data processing, and Transformer models for natural language processing tasks. Results: The experimental data proves that the system demonstrates significant competitive advantages over traditional solutions in terms of increased user satisfaction and improved sales performance. The system achieved 95% accuracy in the product image classification task, 0.15 Mean Absolute Error (MAE) in the user behaviour sequence prediction task, and 88% accuracy in the online review sentiment analysis task. Conclusion: The innovation of this research is to provide a novel deep learning architecture for big data parsing in e-commerce, which points to new paths for future exploration in academia and industry. In addition, the paper proposes corresponding countermeasures for the challenges that may be encountered during system deployment, providing e-commerce enterprises with data-driven decision support to help them make smarter strategic choices in the competitive marketplace.