This paper addresses the issues of insufficient credibility in attribution inference and susceptibility to noise-induced fluctuations in budget allocation in multi-touchpoint marketing scenarios. It proposes a unified framework for marketing attribution inference and budget decision-making agents that incorporates uncertainty modeling. The method uses user interaction paths as sequence input, generating touchpoint weights through sequence encoding and importance modeling. Simultaneously, it outputs the expected incremental contribution and uncertainty characterization at the channel level, extending attribution results from single-point estimation to distributed signals usable for risk measurement. At the decision-making end, a risk-aware budget optimization objective is constructed, coupling contribution expectation and uncertainty penalty into the budget allocation process. Smoothing constraints are introduced to suppress frequent adjustments, forming a closed-loop update mechanism from data to attribution to budget, enabling the strategy to achieve a balance between revenue and stability under constraints. Multi-touchpoint path and cost characteristics are constructed based on publicly available programmatic advertising datasets. An evaluation system covering attribution error, probability calibration, and budget stability is designed. Comparative experiments verify the framework's comprehensive advantages in attribution reliability and budget decision quality, demonstrating the crucial role and engineering usability of uncertainty in the attribution-to-decision transmission process.

This study proposes a prediction model based on a dynamic spatiotemporal causal graph neural network to address the challenges of complex dynamic dependencies, strong structural correlations, and ambiguous causal relationships in corporate revenue forecasting. The model constructs a time-varying enterprise association graph, where enterprises are represented as nodes and industry or supply chain relationships as edges. A graph convolutional network is used to extract structural dependency features, while a gated recurrent unit captures temporal evolution patterns, achieving joint modeling of structural and temporal features. On this basis, a causal reasoning mechanism is introduced to model and adjust potential influence paths among enterprises. A learnable causal weight matrix is used to describe the strength of economic transmission, suppress spurious correlations, and strengthen key causal paths. The model also employs multi-scale temporal aggregation and attention fusion mechanisms to dynamically integrate multidimensional information, enhancing adaptability to both long-term trends and short-term fluctuations. Experimental results show that the proposed model outperforms mainstream methods in multiple metrics, including MSE, MAE, MAPE, and RMAE, verifying its effectiveness in capturing corporate revenue dynamics, modeling economic causal dependencies, and improving prediction accuracy. This study establishes a unified framework that integrates spatiotemporal dependency modeling with causal structure reasoning, providing new insights and methodological foundations for intelligent forecasting in complex economic systems.

This paper addresses the challenges in traditional enterprise performance analysis, including complex multi-source data structures, ambiguous indicator correlations, and poor decision interpretability. It proposes an enterprise performance optimization decision model that integrates knowledge graphs with causal inference. The model constructs a multi-entity and multi-relation knowledge graph to semantically integrate heterogeneous information from financial, market, and operational dimensions, enabling high-level representation of structured relationships among enterprise features. It further incorporates causal structure learning and inference mechanisms to identify key performance drivers and estimate intervention effects, revealing the true causal pathways among variables. In the optimization layer, the model combines knowledge representation with causal relationships to establish an interpretable decision objective function, ensuring that predictions possess both numerical accuracy and causal consistency with logical traceability. Experiments conducted on public enterprise datasets demonstrate that the proposed method outperforms mainstream deep learning and sequence modeling approaches in terms of error control and generalization performance, showing higher robustness and stability. Sensitivity analysis further confirms that the model maintains strong adaptability and consistent performance under different embedding dimensions, noise levels, and optimization strategies. This study provides a novel methodological framework and theoretical foundation for building interpretable and intervention-oriented intelligent decision systems, offering significant implications for data-driven performance evaluation and decision optimization.

To address the difficulty of maintaining stable structural modeling in intelligent decision systems under complex dynamic environments and the sensitivity of decision behavior to state uncertainty, this study proposes an intelligent decision modeling method based on structure aware learning. The method constructs structured state representations that map environmental observations, state evolution relationships, and decision processes into a unified representation space. This design enables explicit modeling of internal structural constraints and dynamic dependencies within the system. During decision modeling, structural constraints are introduced to guide policy learning, allowing decision behavior to remain consistent and stable under long term optimization objectives. The proposed framework effectively captures state evolution patterns in complex state spaces and mitigates decision instability caused by excessive reliance on local information in traditional methods. Comparative evaluations demonstrate clear advantages in decision utility, decision stability, state transition modeling accuracy, and representation consistency. These results indicate that structure aware learning provides a more reliable modeling foundation for intelligent decision systems. The findings further show that systematically integrating structural information into decision modeling improves overall performance and trustworthiness in complex dynamic scenarios.