A method for establishing a risk prediction model, comprising: acquiring training data, the training data comprising a sample region set and annotation results of a risk grade of each sample region in the sample region set and a risk grade of a district to which each sample region belongs; and training an initial model comprising an encoder, a discriminator and a classifier using the training data, and obtaining the risk prediction model using the encoder and the classifier in the initial model after the training process; wherein the encoder performs a coding operation using region features of the sample regions to obtain a feature representation of each sample region; the method being characterized in that the discriminator identifies the risk grade of the district to which the sample region belongs according to the feature representation of the sample region; the classifier identifies the risk grade of the sample region according to the feature representation of the sample region; the initial model has training targets of minimizing a difference of identification of the sample regions belonging to the districts with different risk grades by the discriminator, and minimizing a difference between the identification result of the sample region by the classifier and the annotation result, wherein the region feature of the sample region comprises at least one of: a surrounding preset-type POI feature, a demographic feature, and a user travel feature, and wherein the surrounding preset-type POI feature comprises at least one of: information of a distance between the sample region and a nearest POI of a preset type, and a completeness degree of living facilities in a preset distance range of the sample region; the demographic feature comprises at least one of: a population density condition, a commuting distance distribution, an age distribution, a gender distribution, an income distribution, a consumption ability distribution, an education level distribution, a marital status distribution, a life stage distribution, a job type distribution and an industry type distribution; the user travel feature comprises at least one of: a travel mode, a starting point-destination mode distribution, and a starting point-travel mode-destination mode distribution. acquiring training data, the training data comprising a sample region set and annotation results of a risk grade of each sample region in the sample region set and a risk grade of a district to which each sample region belongs; and training an initial model comprising an encoder, a discriminator and a classifier using the training data, and obtaining the risk prediction model using the encoder and the classifier in the initial model after the training process; wherein the encoder performs a coding operation using region features of the sample regions to obtain a feature representation of each sample region; the method being characterized in that the discriminator identifies the risk grade of the district to which the sample region belongs according to the feature representation of the sample region; the classifier identifies the risk grade of the sample region according to the feature representation of the sample region; the initial model has training targets of minimizing a difference of identification of the sample regions belonging to the districts with different risk grades by the discriminator, and minimizing a difference between the identification result of the sample region by the classifier and the annotation result, wherein the region feature of the sample region comprises at least one of: a surrounding preset-type POI feature, a demographic feature, and a user travel feature, and wherein the surrounding preset-type POI feature comprises at least one of: information of a distance between the sample region and a nearest POI of a preset type, and a completeness degree of living facilities in a preset distance range of the sample region; the demographic feature comprises at least one of: a population density condition, a commuting distance distribution, an age distribution, a gender distribution, an income distribution, a consumption ability distribution, an education level distribution, a marital status distribution, a life stage distribution, a job type distribution and an industry type distribution; the user travel feature comprises at least one of: a travel mode, a starting point-destination mode distribution, and a starting point-travel mode-destination mode distribution. a surrounding preset-type POI feature, a demographic feature, and a user travel feature, and wherein the surrounding preset-type POI feature comprises at least one of: information of a distance between the sample region and a nearest POI of a preset type, and a completeness degree of living facilities in a preset distance range of the sample region; the demographic feature comprises at least one of: a population density condition, a commuting distance distribution, an age distribution, a gender distribution, an income distribution, a consumption ability distribution, an education level distribution, a marital status distribution, a life stage distribution, a job type distribution and an industry type distribution; the user travel feature comprises at least one of: a travel mode, a starting point-destination mode distribution, and a starting point-travel mode-destination mode distribution.