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The purpose is to study the soil's water-soluble organic matter and improve the utilization rate of the soil layer. This exploration is based on the theories of three-dimensional fluorescence spectroscopy, deep learning, and biochar. Chernozem in Harbin City, Heilongjiang Province, is taken as the research object. Three-dimensional fluorescence spectra and a deep learning model are used to analyze the content of water-soluble organic matter in the soil layer after continuous application of corn biochar for six years and to calculate different fluorescence indexes in the whole soil depth. Among them, the three-dimensional fluorescence spectrum theory provides the detection standard for the application effect detection of biochar, the deep learning theory provides the technical support for this exploration, and the biochar theory provides the specific research direction. The results show that the application of corn biochar for six consecutive years significantly reduces the average content of water-soluble organic matter in different soil layers. Among them, the highest average content of soil water-soluble organic matter is "nitrogen, potassium, phosphorous" (NPK) and the lowest is "boron, carbon" (BC). Comparing the soil with BC alone, in the topsoil, the second section (330–380 nm/200–250 nm) with BC + NPK increases by 13.3%, the third section (380–550 nm/220–250 nm) increases by 8.4%, and the fourth section (250–380 nm/250–600 nm) increases by 50.1%. The combination of nitrogen (N) + BC has a positive effect of 20.7%, 12.2%, and 28.4% on sections I, II, and IV, respectively. In addition, in the topsoil, the combination of NPK + BC significantly increases the content of acid-like substances compared with the application of BC alone. In the black soil, with or without fertilizer NPK, there is no significant difference in the level of fulvic acid-like components. The prediction of soil water-soluble organic matter after continuous application of corn biochar based on three-dimensional fluorescence spectra and deep learning is carried out, which has reference significance for the rapid identification and early prediction of subsequent soil activity. [ABSTRACT FROM AUTHOR]