A hybrid identification model based on multilayer artificial neural networks （ANNs） and particle swarm optimization （PSO） algorithm is developed to improve the simultaneous identification efficiency of thermal conductivity and effective absorption coefficient of semitransparent materials. For the direct model， the spherical harmonic method and the finite volume method are used to solve the coupled conduction-radiation heat transfer problem in an absorbing， emitting， and non-scattering 2D axisymmetric gray medium in the background of laser flash method. For the identification part， firstly， the temperature field and the incident radiation field in different positions are chosen as observables. Then， a traditional identification model based on PSO algorithm is established. Finally， multilayer ANNs are built to fit and replace the direct model in the traditional identification model to speed up the identification process. The results show that compared with the traditional identification model， the time cost of the hybrid identification model is reduced by about 1 000 times. Besides， the hybrid identification model remains a high level of accuracy even with measurement errors.