Concerning the problem of identification of parameters and time delay for chaotic systems with unknown delay， a parameter identification model for time-delay chaotic systems based on temporal attention mechanism was proposed， namely Parameter Identification Neural Network with Temporal Attention （PINN-TA）. Firstly， the time delay identification was implemented by applying temporal attention mechanism to extract correlation features within system state sequences. Then， the algebraic equations of system parameters were formed by implicitly approximating system differential equation with the use of recurrent neural network. Finally， the roots of these equations were taken as the results of parameter identification. With typical time-delay chaotic systems including delay Logistic equation， Ikeda differential equation and Mackey-Glass chaotic system used as identificated objects， PINN-TA model was compared with multiple intelligent search algorithms in experiments. Simulation results show that PINN-TA model has the identification error of parameters and time delay 90.31% to 99.36% lower in comparison with existing intelligent search algorithms such as Artificial Raindrop Algorithm （ARA）， Hybrid Cuckoo Search （HCS）， Global Flower Pollination Algorithm （GFPA） and Cellular Whale Algorithm （CWA）， while the identification time of the proposed model is shortened to 18.59 to 19.43 ms. It can be seen that PINN-TA model can meet the accuracy and real-time requirements， and provides a feasible solution for identification of parameters and time delay for time-delay chaotic systems.