## 4 Results and Discussion
### Discussion  
#### 4.4.1 Empirical Findings and Behavioral Insights  
A series of experiments was conducted to systematically evaluate the proposed model from three perspectives: overall performance, module contributions, and robustness across different subsets of behavior data. The results demonstrated that the proposed framework exhibits strong adaptability, stability, and generalization capabilities under complex economic behavioral scenarios. In real-world applications, platform-level decision-making is often challenged by high-dimensional heterogeneity, delayed feedback, and volatile user behavior. Traditional static or shallow learning models fail to effectively integrate such multi-source, heterogeneous information. The proposed approach, constructed and validated on large-scale daily-granularity behavior data from e-commerce platforms, exhibits practical potential for deployment. In terms of overall performance, the proposed strategy-generation framework significantly outperformed mainstream models such as XGBoost, BERT, LSTM, transformer, and standard DQN across all metrics, including total profit gain, average reward, behavioral output stability, and profit-price curve fitting. In the dataset collected throughout 2023 from jd.com, taobao.com, suning.com, and vip.com, numerous occurrences, such as temporary promotions, unexpected demand surges, and inventory fluctuations, were observed. These patterns require models with strong temporal sensitivity and adaptive policy adjustment capabilities. Unlike traditional methods that tend to respond prematurely or belatedly due to rigid weighting schemes, the proposed model demonstrated the ability to generate stable, trend-aware policy trajectories in the presence of highly imbalanced and volatile economic signals. For instance, in datasets related to holiday promotions for consumer electronics, the proposed method responded more accurately to changes in click behavior and peak historical sales, maintaining continuity and an upward trajectory in profit outcomes. Among the four behavior data subsets, the model performed best on sales feedback and pricing records data, indicating a particular sensitivity to features exhibiting numerical continuity and strong price-sales coupling. In the sales feedback subset, the model captured periodic patterns and temporal surges in sales, effectively avoiding policy instability at peak or off-season transitions, which is critical for datasets involving cyclical categories such as food and personal care. Although the performance was slightly lower on the browsing behavior and inventory status datasets, the proposed method still outperformed all baseline models. The browsing dataset, characterized by high uncertainty and weak click-conversion correlation, presented a low signal-to-noise ratio. While the model could not fully predict post-click conversion behavior, the combination of perturbation augmentation and policy stabilization successfully suppressed the influence of behavioral outliers. In inventory modeling, the model estimated potential stockout risks by learning from inventory update frequencies and outbound flow patterns, enabling timely policy adjustments. This feature proved particularly valuable in domains prone to stock discontinuities, such as books and home goods. Regarding module contributions, the ablation study confirmed the distinct value of the three key modules in practical scenarios. The perturbation augmentation module introduced Gaussian and adversarial noise to price shifts, sales fluctuations, \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **Total Profit Gain** & **Average Reward** & **Std. of Actions** & **R2 Score** & **Sharpe Ratio** \\ \hline XGBoost & 0.08 & 2.91 & 1.03 & 0.52 & 0.64 \\ LSTM-based & 0.14 & 3.45 & 0.84 & 0.67 & 0.71 \\ Transformer-based & 0.18 & 3.88 & 0.74 & 0.75 & 0.76 \\ DQN & 0.21 & 4.01 & 0.60 & 0.78 & 0.79 \\
**Proposed** & **0.27** & **4.49** & **0.42** & **0.85** & **0.83** \\ \hline \hline \end{tabular}
\end{table}