Aiming at the problem of scarcity of expert knowledge required by knowledge-driven demand forecasting model and insufficient interpretability of data-driven demand forecasting model, a fuzzy prediction model of spare parts demand driven by data and knowledge was proposed. Based on the fuzzy clustering algorithm, the numerical data was clustered into a rule base with simple structure and strong interpretability. The domain expert knowledge was represented as a Mamdani-type rule base by utilizing fuzzy logic. On this basis, a new type of intelligent computing theory—fuzzy network theory was introduced, the two types of rule bases were merged into an initial prediction model. A genetic algorithm was employed to optimize the fuzzy set parameters of the model′s rule base to enhance the model′s predictive accuracy. Compared with the fuzzy clustering algorithm, the proposed model has advantages in interpretability and accuracy.