China is one of the countries with the most serious geological disasters in the world. Geological disasters occur frequently and are of various kinds. In recent years, the superimposed effects of extreme weather have accelerated the frequency and degree of various geological disasters, resulting in increasingly difficult monitoring and warning, emergency rescue, and postdisaster recovery and reconstruction of various geological disasters. How to introduce new information technology to carry out indepth analysis and utilization of geological disaster data has become a breakthrough in future geological disaster prevention and control. To carry out the research and analysis of geological disaster data comprehensively and accurately, this paper first analyzes the research background of geological disasters in China and the current development status of retrieval technology. Then, fromthe perspective of data types and data characteristics, this paper summarizes geological disaster data, systematically introduces three data retrieval technologies, namely the principal component analysis method, convolutional neural network method, and hash method, and takes the “1.22” landslide geological disaster in Zhenxiong, Yunnan in 2024 as an example. The data related to the landslide disaster in the microblog were retrieved and analyzed, and the cluster visualization was displayed. In addition, the research results guided the application and development of retrieval technology in the field of geological disaster prevention and control. Finally, from the aspects of future principal component analysis method improvement, hash method optimization, and limitation of thinking, it is proposed to strengthen the integration of data retrieval technology and geological disaster emergency response so as to continuously meet the needs of massive disaster data on retrieval speed and information processing performance.