In order to prevent gas polyethylene (PE) pipeline leakage accident caused by abnormal thermal oxidative aging, an ultrasonic intelligent detection method for thermal oxidative aging degree of pipeline was proposed. The linear and nonlinear ultrasonic testing characteristic parameters of PE pipe wereextracted, and the relationship between the characteristic parameters and the aging degree of PE pipe was analyzed. The BP neural network was constructed and optimized, and the ultrasonic testing characteristic vector and aging time were respectively used as the input and output of the network. The testing characteristic parameters were expanded and used for network training, and the neural network model that could evaluate the aging degree of PE pipe was obtained. The results show that the acoustic velocity, acoustic attenuation and nonlinear coefficient can be used to form the characteristic vector of ultrasonic testing for intelligent evaluation of aging degree. The optimized and trained neural network can be used to evaluate the aging time of PE pipe, and the average relative error of the evaluation value of aging time is less than 6.9%.