The invention discloses a roller kiln temperature prediction integrated modeling method capable of combining a mechanism with data. The method comprises the following steps that: through the analysis of factors which affect temperature change, from a perspective of the temperature change and energy, establishing a mechanism model; then, considering situations that roller kiln sintering is a very complex process, a whole sintering process can not be described through a single mechanism model and the mechanism model has model errors through simplification, establishing a data model to predict model errors so as to make up mechanism output, i.e., utilizing errors to serve as a training sample to establish an error prediction model of a nonlinear time-varying process based on local weighted kernel principal component regression; and finally, combining the mechanism model with the data model to establish a roller kiln temperature prediction integration model. By use of the model, the state change of a process can be better tracked, and a good guidance function is provided for roller kiln temperature control so as to improve the production quality and the percent of pass of a product.