No abstract

No abstract

Mineral resources are indispensable in the development of human society and are the foundation of national economic development. As the prospecting target shifts from outcrop ore to concealed ore, from shallow to deep, the difficulty of prospecting becomes more and more difficult. Therefore, the prediction of mineralization prospects is of great significance. This paper is aimed at completing the prediction of mineralization prospects by constructing geological semantic models and using mobile computer learning to improve the accuracy of prediction of mineralization prospects and expanding the application of semantic mobile computing. We use five different semantic relations to build a semantic knowledge library, realize semantic retrieval, complete information extraction of geological text data, and study mineral profiles. Through the distributed database of mobile computing, the association rules and random forest algorithm are used to describe the characteristics of minerals and the ore-controlling elements, find the association rules, and finally combine the geological and mineral data of the area and use the random forest algorithm to realize the prospect of mineralization district forecast. The geological semantic model constructed in the article uses the knowledge library for associative search to achieve an accuracy rate of 87.9% and a recall rate of 96.5%. The retrieval effect is much higher than that of traditional keyword retrieval methods. The maximum value of the posterior result of the mineralization prospect is 0.9027, the average value is 0.0421, and the standard deviation is 0.1069. The picture is brighter, and the probability of mineralization is higher.