With the acceleration of global climate change and urbanization, urban resilience has become a critical issue. This study, based on the Pressure-State-Response (PSR) model, constructs an urban resilience evaluation index system for Sanming City. Indicator weights are determined by combining the Analytic Hierarchy Process (AHP) and the entropy weight method. Spatial analysis methods, such as spatial autocorrelation, kernel density estimation, standard deviation ellipses, and geographic detectors, are employed to explore spatial–temporal analysis and driving factors of urban resilience. The results show the following: (1) from 2014 to 2022, Sanming’s urban resilience index initially increased and then declined; (2) the spatial distribution of urban resilience is uneven, with high-resilience areas concentrated in the city center and southeast, while the northwest is relatively low; (3) Local Moran’s I analysis confirms significant positive spatial autocorrelation, with regional differences gradually expanding; (4) geographic detector analysis reveals that NDVI, monthly maximum precipitation, nighttime light index, annual average PM2.5 concentration, and impervious surface ratio are key drivers of urban resilience; (5) factor interactions show nonlinear enhancement, with ecological and climatic–environmental factors interacting as key drivers of urban resilience changes.

With the diversification of urban travel patterns, accurate short‐term passenger flow forecasting has become critical for efficient metro operation and management. While existing studies primarily focus on temporal patterns within passenger flow data, the influence of external factors remains underexplored. To bridge this gap, this study develops a forecasting model based on the long short‐term memory (LSTM) network, informed by an analysis of typical passenger travel characteristics. The model is applied and validated using passenger flow data from Shenzhen North Subway Station for December 2024, demonstrating its applicability and high accuracy. Furthermore, correlation analysis is conducted to quantify the impact of external factors, including holidays and weather conditions. Results indicate that weekday/weekend patterns significantly influence flow, rainy weather exerts a notable negative impact, while temperature and wind speed show only minor effects. Beyond the specific case, this study provides a reproducible and extensible analytical framework for integrating multi‐source temporal data and quantifying the impact of external factors on passenger flow dynamics.

Geological hazard data exhibits high-volume and multi-type characteristics, specifically characterized by inherent complexity; measurement uncertainty; cross-source heterogeneity; underdeveloped semantic organization; and fragile inter-entity associations. Consequently, advanced modeling techniques coupled with robust extraction frameworks become imperative for effective unstructured data governance. To address this challenge, we propose a content–knowledge representation framework that decomposes and reconstructs disaster data using fine-grained content entities as base units. This approach allows for a unified description, objectification, ordering, hierarchical storage, and indexed categorization of unstructured information. Furthermore, we develop specialized text extraction algorithms tailored to document imagery and vector maps—facilitating the systematic application of information retrieval techniques while efficiently targeting specific thematic content. Our method outperforms two representative deep learning architectures (Fast CNN and FCN), demonstrating superior performance in segmenting target regions and precisely detecting textual elements, tables, and geographic features within complex datasets. By studying the modeling and extraction technology of unstructured geologic data, this paper establishes the value chain of geologic result data, which can provide strong support for digital management of geologic disaster data and improve work efficiency.