A method for processing a user request, the method comprising: receiving a user request; sending the user request to a target prediction model stored in a local secure container of a data user, wherein the local secure container is created in a local storage space by using Software Guard Extensions technology, and the target prediction model is obtained by training an initial prediction model with an encrypted feature sample and a labeled result sample corresponding to the encrypted feature sample, and the encrypted feature sample is a feature sample obtained after a feature data provider encrypts a plaintext feature sample provided by the feature data provider, and transmitted by the feature data provider through a ciphertext transmission path established between the feature data provider and the local secure container, and a training sample of the target prediction model is composed of two parts coming respectively from two different execution bodies, one is a directly user-oriented data user directly providing a service to the user, and the other is the feature data provider providing the user-oriented data user with partial feature data for obtaining a prediction result and being missed from the data user; creating the target prediction model and storing the target prediction model on the data user; receiving the prediction result output by the target prediction model; determining an actual risk rating of a user corresponding to the user request according to the prediction result; and returning response information indicating that the user request is not passed, in response to the actual risk rating being not higher than a preset rating. receiving a user request; sending the user request to a target prediction model stored in a local secure container of a data user, wherein the local secure container is created in a local storage space by using Software Guard Extensions technology, and the target prediction model is obtained by training an initial prediction model with an encrypted feature sample and a labeled result sample corresponding to the encrypted feature sample, and the encrypted feature sample is a feature sample obtained after a feature data provider encrypts a plaintext feature sample provided by the feature data provider, and transmitted by the feature data provider through a ciphertext transmission path established between the feature data provider and the local secure container, and a training sample of the target prediction model is composed of two parts coming respectively from two different execution bodies, one is a directly user-oriented data user directly providing a service to the user, and the other is the feature data provider providing the user-oriented data user with partial feature data for obtaining a prediction result and being missed from the data user; creating the target prediction model and storing the target prediction model on the data user; receiving the prediction result output by the target prediction model; determining an actual risk rating of a user corresponding to the user request according to the prediction result; and returning response information indicating that the user request is not passed, in response to the actual risk rating being not higher than a preset rating. <EOS>