放火案件是典型的严重暴力犯罪案件，严重影响社会安全与公共安全。放火案件的侦破难度较高，传统经验已无法满足案件快速准确侦破需求。该文旨在构建通用案件侦查模型，指导案件的侦查工作。研究通过梳理放火案件的条件要素、行为要素和后果要素，应用Bayes网络分析方法，构建放火案件侦查模型，并应用典型案例对模型进行有效性验证。结果表明，本研究构建的放火案件侦查模型可以有效还原案件发生情景，给出正确的案件推理假设。该模型可以协助刑侦人员开展放火案件的侦查工作，提高侦查效率，支撑智慧侦查实践。

Objective: Criminal cases constitute one of the primary threats to social security. Serious violent crimes, such as arson, pose a substantial risk to public safety. Moreover, the highly destructive nature of arson scenes and the vulnerability of evidence to damage or destruction significantly complicate fire investigations, making arson cases particularly difficult to solve, especially for investigators with limited experience. Faced with increasingly complex and evolving criminal patterns, traditional investigative approaches, primarily dependent on accumulated experience and manual screening, are no longer sufficient to meet the growing demand for rapid and accurate resolution of arson cases. Consequently, the development of a universal investigative reasoning model for arson cases has become a critical priority requiring urgent attention. Therefore, this study aims to construct a generalized investigative model to guide arson case investigations and support intelligent and standardized collection of evidence. Methods: The study first analyzed the key elements of arson cases by examining both the conditions under which the criminal act occurred and its resulting consequences. The conditions under which a criminal act occurs were defined as conditional elements; the criminal act itself was defined as the behavioral element, and the resulting consequences were defined as outcome elements. Subsequently, an element correlation model was constructed to analyze the relationships among conditional, behavioral, and outcome elements in arson cases. The element correlation model further decomposed physical evidence into two components: item-trace elements and their associated relationships. Then, a Bayesian network inference model was employed to establish an investigative framework for arson cases. Results: The proposed model was validated through analyses of representative case studies. First, a simulated case was used to illustrate the application procedure and the effectiveness of the model. Through this case study, the process of constructing Bayesian network nodes and their interconnections for arson investigations was examined. Bayesian probability calculations were performed to estimate the likelihood that each conditional element hypothesis was true. Furthermore, the practical case of the "Xiamen Bus Arson Incident" was used to demonstrate the model's effectiveness in addressing complex real-world cases. Through a phased analysis of available investigative data and the recommendations generated by the model, combined with comparisons against actual case conditions, the application process of the model was demonstrated. The result showed a high degree of accuracy in constructing the incident and supporting investigative reasoning. Conclusions: The results demonstrate that the proposed investigative model can effectively reconstruct incident scenarios and generate accurate investigative hypotheses. The model can assist criminal investigators in handling arson cases, improve investigative efficiency, and support the development of intelligence-led investigative systems. Notably, the investigative reasoning model proposed in this study focuses primarily on directly quantifiable crime scene elements and behavioral causal chains and does not yet incorporate more complex criminal motivations into the reasoning framework. Although the model primarily addresses reasoning processes during the investigation phase of arson cases, it provides limited consideration of the evidentiary standards associated with evidence collection. In practical applications, the model primarily provides analytical support during the preliminary stages of an investigation, facilitating the intelligent and standardized collection of evidence rather than replacing the expertise and judgment of professional criminal investigators.