The present invention discloses a mixed modeling method and system based on the combination of process priors and a data-driven model. The method comprises: selecting a data-driven model and an appropriate model structure from known data-driven models so as to establish a mathematic relation expression corresponding to the data-driven model, and arraying all model parameters in a certain order; verifying the process priors of the model so as to obtain a constraint equation of the degree of detected model against the process priors; comparing the model output of the sample with observed values, and establishing an optimal target equation for detecting the degree of the model fitting the training sample; combining the constraint equation with the optimal target equation so as to establish a constraint-optimization problem; solving the optimal parameter solutions by adopting an intelligent algorithm of constraint processing; adopting the obtained optimal parameter solutions as the model parameters of S1, and bringing the obtained optimal parameter solutions in the original model so as to predict or optimize the model. Through the adoption of the mixed modeling method disclosed by the present invention, the model which more conforms to priori knowledge can be obtained in the neural network training of a small amount of data samples, so that the over-fit phenomenon can be avoided.