## 1 Introduction  
In recent years, the rapid advance of urbanization and the widespread adoption of diverse transport modes have led to traffic accident data characterized by high dimensionality, multi-source heterogeneity, and complex structures [1], posing unprecedented challenges for accident analysis and prevention. These data--sourced from traffic surveillance systems, sensor networks, and social media--often suffer from uneven quality, missing values, redundancy, and noise [2], greatly complicating cleaning and preprocessing. Furthermore, significant discrepancies in temporal synchronization and spatial alignment across data sources make effective information fusion a critical technical hurdle. The accident process itself exhibits pronounced spatiotemporal dynamics and uncertainty, involving complex nonlinear interactions among driver behavior, road environment, and meteorological conditions, which further impedes the development of high-precision predictive models and effective countermeasures. To achieve substantive progress in accident prediction, prevention strategy formulation, and risk assessment, an urgent need exists for a framework that can both integrate multi-source data and capture fine-grained semantic representations, thereby more accurately supporting risk prediction and safety decision-making. In earlier research on traffic accident data processing, traditional statistical analysis methods predominated. For example, [PERSON] [3] classified causes of death according to ICD-10 and employed Excel 2016 and SPSS 19.0 for data analysis, revealing that motor vehicle accidents accounted for approximately 20.90% of fatalities in the Haidian District. [PERSON] et al. [4] examined shortcomings in road traffic accident data mining--specifically incomplete data, unreasonable criteria for identifying high-incidence locations, and a narrow dimensional scope for causal analysis--and proposed an optimized set of data-collection items together with a GIS-based, multidimensional method for accident cause analysis. [PERSON] et al. [5], using a large dataset of accidents in Guiyang, developed a novel practical numerical model grounded in rough-set theory to assess the significance of objective factors and thus mined the key determinants of road traffic accidents. Although these conventional approaches facilitate straightforward processing of traffic accident data, they typically rely on linear relationship assumptions and are therefore ill-suited to capture the complex nonlinear correlations and multivariate interactions inherent in accident data. Given the high dimensionality, multi-source heterogeneity, and spatiotemporal dynamics of such datasets, traditional models often fail to fully reflect the underlying mechanisms of accident occurrence. Moreover, they tend to be sensitive to noise, missing values, and outliers, and their robustness and predictive accuracy are compromised when fusing data from disparate systems, limiting their utility for effective accident prevention and risk assessment. To address the limitations of traditional statistical methods, researchers have adopted machine learning and deep learning techniques--including Bayesian frameworks, support vector machines (SVMs), and Long Short-Term Memory (LSTM) networks. [PERSON] et al. [6] applied the Apriori algorithm, Bayesian theory, and fuzzy clustering for large-scale data mining to impute missing accident attributes, assess accident severity, and classify risk levels, validating their approach on Shenzhen traffic accident records from 2014 to 2016. [PERSON] et al. [2] developed a hybrid SVM model that integrates multi-source influencing factors and a deep-neural-network-based dynamic adaptive recognition algorithm to identify urban accident hotspots. [PERSON] et al. [7] employed machine learning and a stacked sparse autoencoder (SSAE) to predict the severity of traffic accident injuries. [PERSON] et al.