Abstract:So far, the existing method for automatically discriminating information is difficult to reflect the impact of mixing quantitative and qualitative indicators of the combination of the water layer recognition. Therefore, in order to improve the accuracy of determining flooded layer, this paper proposes neural network model to calculate quantitative and qualitative transformation hybrid cloud-based discrimination to achieve flooded layer. On the one hand, qualitative information is extracted logs by cloud model, to ensure the integrity and objectivity of the original data; on the other hand, the information in the qualitative concept forward through the normal cloud is converted to quantized transform numerical information, ensures the scientific data; the eventual establishment of correspondence between the characteristics of the system input and results. Experimental results show the high accuracy of the calculation method for water layer recognition neural network based on hybrid cloud transformation, it has the characteristics of fast, flooded layer identification is a more practical approach.