Variance Analysis in China's Coal Mine Accident Studies Based on Data Mining.

Area of Science:

• Mining Engineering
• Safety Science
• Data Mining

Background:

• Coal mine accident risk increases with depth, necessitating scientific analysis and advanced technologies for prevention.
• Understanding research progress and focus differences in coal mine accidents is crucial for improving disaster management and prevention.
• Global research on coal mine accidents is predominantly published in Chinese or English.

Purpose of the Study:

• To analyze the research progress and focus differences in coal mine accident studies between China and other countries.
• To identify shortcomings in current research and promote more effective coal mine disaster management.
• To enhance the prevention and control capabilities for coal mine accidents.

Main Methods:

• Analysis of Chinese and foreign literature on coal mine accidents.
• Application of data mining algorithms, specifically Latent Semantic Indexing (LSI) and Apriori.
• Comparative analysis of research trends, author contributions, and thematic focuses.

Main Results:

• Chinese authors dominate coal mine accident research, contributing over 81% of authors.
• Chinese studies focus on macro-scale accident patterns and causes, while English studies concentrate on occupational injuries and specific disaster mechanisms.
• Chinese research increasingly incorporates AI for deep mining safety since 2018, while English research hotspots remain consistent.
• Both Chinese and English studies explored 'public opinion' around 2019, with differing focuses on government guidance versus critical analysis.

Conclusions:

• China leads global research in coal mine accident prevention and control, with a distinct focus on technological integration and macro-level analysis.
• Divergent research priorities between Chinese and English literature highlight opportunities for knowledge exchange and collaborative advancement.
• The evolution of Chinese research reflects the country's industrial development and policy shifts, particularly the adoption of AI in deep mining safety.