In traditional quadrotor PID controller, parameter tuning is difficult and it is also difficult to achieve optimum control effect.To solve the problems, we constructed a quadrotor PID Neural Network (PIDNN) controller, which integrated the advantages of the traditional PID controller of clear engineering meaning and simple parameter tuning, with the advantages of Neural Network (NN) of nonlinear mapping and self-learning capability.The nonlinear mapping and self-learning capabilities of NN were used to optimize the control effect of traditional PID controller.By constructing the PID controller, the initial values of number of neural network layers, modes and connection weights were determined.At the same time, we designed a kind of adaptive flight control algorithm of PIDNN, using adaptive adjustment of proportional neuron weighting coefficient to increase the response speed of the system.The rationality and validity of the algorithm were verified by using a nonlinear full numerical simulation.