This paper proposes a fitness prediction method based on transfer learning grey support vector regression with the clustering evolution strategy aiming at machine perception evaluation and population evolution, which can minimize knowledge model differences by sharing the user learning model of individual evaluation fitness and the part of unevaluated individuals, and can forecast unevaluated individuals fitness. A selection and crossover operator based on clustering individual average distance are proposed, which can expand offspring search area and enhance population diversity. Based on above strategies, the interactive evolutionary computation applied to NSGA-II is proposed. Finally, the analysis of the time complexity indicates that the proposed method can improve evaluation precision, and overcome the local convergence problem. The method is applied to interior light toning optimization problem, and its outstanding performance is experimentally demonstrated.