Considering the disadvantages of the direct inverse model for the back propagation (BP) neural network, such as low precision, excessive time consumption, and easy to concussion, this paper proposes an inverse modeling method for the BP neural network that combines an improved ant colony algorithm and a Bayesian regularization algorithm. This method improves the ant colony algorithm, which sets the volatilization factor based on the search stage, updates the pheromone based on the degree of pros and cons of the path, and considers the distance between the starting point and the nodes and the distance between the end point and the nodes in the heuristic factor, to optimize the weight of the forward model and improve the accuracy of the overall model. Then the Bayesian regularization algorithm with L_1/2 norm is used to reverse the input of the forward model, which improves the stability of the network. It is applied to a reconfigurable power amplifier. Experimental results show that the accuracy of the method, compared with that of the direct inverse modeling method and the adaptive η inverse modeling method, is improved by 99.77% and 90.70%, respectively, with the average running time for the modeling being shorten by 35.76% and 2.05%, respectively. Thus, the complexity of designing a power amplification module is reduced and its design speed is accelerated.