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Abstract: Data fusion is one effective method to improve the precision of data processing. The weight and parameter estimation of unequal-precision data fusion was studied. For the linear model, the optimal weight is only determined by the precisions of measured data, which is consistent with the classic Gauss - Markov theorem. For nonlinear model, the optimal weight and algorithm of the multi-structure unequal-precision nonlinear regression model are established according to the curvature representation of the estimation mean square error. Numerical simulation results show that the weight of the nonlinear model has great influence on the precision of parameter estimation. The optimal weight is related to both the statistical feature of data and the model structures, such as the model curvature, sampling number, etc. In this case, the weight (only related to the precision of measured data) obtained based on the Gauss-Markov theorem for the linear model is no longer optimal. The proposed method was verified by examples, and its effectiveness was proved.