## 1 Introduction
Compared with conventional approaches, DQN exhibits superior generalization performance and adaptability to dynamic market environments. For example, [PERSON] et al. constructed a Dueling DQN-based presale pricing model capable of adapting to complex dynamics in multi-phase sales processes, which demonstrated strong policy generalization across various product categories, thereby improving retail profitability [24]. [PERSON] et al. developed a reinforcement learning framework for multi-flight pricing, validating the policy convergence and generalization performance of DQN and other algorithms under high-dimensional state spaces and competitive market conditions such as high-speed rail interference [25]. [PERSON] et al. further demonstrated the profit advantages of DQN in high-volatility pricing scenarios within intelligent transportation systems [26]. Recent studies have also extended DRL to incorporate more diverse behavioral signals, such as inventory levels, competitor pricing, and user browsing behavior [27]. Effectively integrating these multidimensional features remains a core challenge in pricing-strategy modeling. To address this issue, [PERSON] et al. proposed a multimodal financial embedding approach to fuse price and sentiment data while enabling interpretable weighting of each modality's influence on the decision outcomes [28]. [PERSON] et al. designed a multi-source information fusion framework that integrates graph convolutional networks and large language models for sector rotation-based asset allocation, demonstrating strong performance under multi-source financial data scenarios [29]. [PERSON] et al. developed a high-dimensional state representation framework capable of capturing cross-feature interactions and enhancing the robustness of pricing decisions [30]. In addition, hybrid models have also been explored. [PERSON] et al. introduced a K-means-LSTM hybrid model based on dynamic time warping (DTW) clustering to predict financial time series[31]. [PERSON] et al. developed a DDPG-based pricing strategy for electric vehicle aggregators under evolving market conditions[32]. Moreover, [PERSON] et al. integrated DRL with game-theoretic reasoning to derive optimal pricing strategies under strategic competition using Nash equilibrium solutions[6]. [PERSON] et al. proposed a hybrid DRL model based on a bi-level Markov decision process that integrates day-ahead and real-time pricing mechanisms for coordinated modeling of supply-demand dynamics in electricity markets [33]. These recent developments underscore the increasing structural sophistication of DRL-based pricing frameworks and highlight the importance of designing architectures capable of learning from heterogeneous, delayed, and interactive economic feedback signals. A novel framework for pricing-strategy generation and revenue prediction based on DRL is proposed in this work. By introducing a deep Q-network architecture and incorporating high-dimensional state modeling, price perturbation enhancement, and delayed-reward reevaluation mechanisms, the framework enables dynamic responses to user behavior and progressive optimization of pricing strategies. The key contributions of this work are summarized as follows:  
1. _High-dimensional heterogeneous state modeling and long short-term return balancing:_ A DRL-based pricing framework is proposed that effectively addresses the challenges of modeling high-dimensional heterogeneous states and balancing short- and long-term returns using large-scale economic behavior data. 2. _Reward enhancement via price perturbation and delayed-feedback revisitation:_ A novel reward enhancement mechanism is designed based on price perturbation and delayed-feedback tracing, significantly improving model stability and applicability in real-world big data environments, especially under delayed-feedback and sparse-data conditions. 3.