As the traditional measuring method based on dielectric coefficients shows cross-sensitivity for multi-parameters in the measurement of oil/water two-phase flows, it can not meet the requirements of real-time optimization control for petroleum production. Therefore, this paper investigates a method to measure multi-parameters with cross-sensitivity by using multi-sensing technology.It presents an inverse model of wavelet neural network with genetic optimization and also researches its identification method. The model overcomes the blindness of initialization weight-value choice in traditional neural networks, provides the abilities of global optimization and nonlinear self-learning, and eliminates the cross-sensitivity of multi-factors. The simulation and experimental results demonstrate the validity and effectiveness of the proposed model and show that the correlation coefficient between the predicted values and calibration values is 0.999 6, which is better than that of BP-NN model. The method has strong generalized capability and robust convergence rate, and can effectively eliminate the influence of the cross-sensitivity of multi-factors and the nonlinearity of sensor self on the measuring precision, and improve the dynamic characteristics and measurement accuracy of sensor systems.