The invention provides a chaotic search optimization method for traffic flow prediction of an adaptive neural network. The method comprises the following steps: S1, constructing a BP neural network model, and initializing network parameters; s2, initializing various parameters of a sparrow algorithm; s3, adding a Tent chaotic mapping initialization population; s4, calculating the fitness value of the sparrows in the population; s5, sorting the populations according to the fitness values; s6, updating the position of the producer; s7, updating the position of the follower; s8, updating the position of the sparrow in danger; s9, updating the optimal fitness value of the individual, then updating the optimal fitness value of the group, and entering the step S10; s10, judging whether the number of iterations is reached or not, and if not, returning to the step S5; otherwise, outputting the optimal fitness value and the global optimal position, and entering the step S11; and S11, endowing the optimal fitness value and the global optimal position to the BP neural network model, optimizing the weight and the threshold value of the BP neural network model, and performing prediction to complete the construction of the CSSA-BP model. The method is higher in prediction accuracy and higher in iteration speed.