Abstract: There are many challenges in the task of predicting ore deposits from big data repositories. The data are inherently complex and have great significance to the intervenient spatial relevance of deposits. The characteristics of the data make it difficult to use machine learning algorithms for the quantitative prediction of mineral resources. There are considerable interest and value in extracting spatial distribution characteristics from two-dimensional ore-controlling factors'layers under different metallogenic conditions. In this paper, the authors conducted such an analysis by using a Deep Convolutional Neural Network (D-CNN) algorithm named AlexNet. Training on the two-dimensional (2-d) mineral prediction and classification model was performed using data from the Songtao-Huayuan sedimentary manganese deposit. The authors investigated the coupling correlation between the spatial distribution of manganese element, sedimentary facies, outcrop of Datangpo Formation, faults, water system and the areas where manganese orebodies are present, as well as the correlation between different ore-controlling factors by employing the AlexNet networks. After training, the deep convolutional neural network classification model with the verification accuracy of 88.89%, recall of 66.67% and loss value of 0.08 could be obtained. By applying this model to unknown areas for two-dimensional metallogenic prediction, four metallogenic prospective areas. i.e., No. 91, No. 96, No. 154 and No. 184, were delineated, in which the ore potential probability of No. 91 regional ore-bearing probability and No. 154 prospective area is 1, and that of No. 96 is 0.5, suggesting that the probability of existence of undiscovered deposits in prediction areas is large.