Abstract: As a general form of classical adjustment model, general errors-in-variables (EIV) adjustment model has the advantage of taking into account multiple random errors. Based on the linear estimation of the weighted total least squares of the general EIV adjustment model, the regularization criterion is introduced. When the regularization matrix is the unit matrix, it is called the ridge estimation. The objective function is then added. By establishing the minimization solution of the Lagrange objective function, the ridge estimation solution corresponding to the weighted general EIV adjustment model is derived. The U curve method and L curve method for determining ridge parameters are given. The linear estimation, two ridge estimations and their corresponding variance components of the general EIV adjustment model are calculated. It is validated that ridge estimation can promote the linearization estimation of general EIV model, reduce the times of iterations, make the parameter variance component more stable and reduce the calculation of parameter estimation.