The Co-based alloy coating was prepared by laser cladding on GCr15 bearing steel with fiber laser. The effects of laser power, scanning speed and powder feeding rate on the dilution ratio of the coating were studied by orthogonal experiment. According to the results of orthogonal test, RBF neural network was used to establish the prediction model between the laser process parameters and the dilution rate of cladding layer, then the network was tested with test samples. The results show that the most significant factor affecting the dilution rate is the powder feeding rate. Due to the energy threshold and "heat shield" effect of powder melting, the dilution rate does not increase or decrease with the increase of laser power and powder feeding rate, there are some fluctuations；With the increase of scanning speed, the dilution ratio decreases gradually, and the variation of dilution ratio is determined by the interaction of various cladding process parameters. The RBF neural network model trained by the experimental data can predict the dilution ratio of the Co-based cladding layer prepared under different laser processing parameters. The relative error between the predicted value and the measured value is within 6%, which has a high prediction ability.