Construction and prediction of a prospecting model based on recurrent neural network

Under the background of big data and artificial intelligence and on the basis of the establishment and application basis of existing traditional geological prospecting model, this paper proposes a prospecting model construction and prediction method based on cyclic neural network, with the purpose of achieving in-depth analysis and understanding of geological data. According to the requirements for construction and prediction of geological prospecting model, the authors combined the data cleaning theory to systematically summarize and summarize the traditional geological prospecting model, thus establishing a geological prospecting knowledge base and providing training data for deep learning algorithms. The accuracy of the comparison results and the time used for classification were comprehensively analyzed. Finally, the RNN classification algorithm was selected to classify the conceptual model of prospecting. In the process of establishing the prospecting model of the study area, by using the key words and ore control elements to complete the model matching, the model was used to analyze the model matching results so as to realize the construction of the regional geological prospecting model and the prediction and analysis of the mineral resources. With the Dashui gold deposit as an example, the construction of the prospecting model was realized quickly and accurately, which effectively provides guidance for the prediction of mineral resources and verifies the feasibility of the method.