For the problem that the traditional neural network algorithm cannot effectively use a small amount of actual flight data, a fitting algorithm of airborne weapon attack area based on confidence neural network is proposed. In order to effectively distinguish and use different confidence samples, a more accurate fitting network is obtained by calculating the sample error capacity interval and setting the sample sensitivity coefficient, modifying the network prediction error used for weight adjustment. The simulation results show that, compared with the traditional neural network algorithm, the confidence neural network algorithm has higher estimation accuracy and increases the success probability of fitting attack area.