This study comprehensively investigates the parameter extraction flows for Hot Carrier Degradation (HCD) in advanced technology based on Neural Network (NN). The emerging NN-based approach and the conventional particle swarm algorithm are applied for the extraction of HCD-related parameters in the BSIM-CMG model, respectively. As verified by 16/14nm FinFET data, HCD-induced degraded characteristics can be well extracted. Taking the PSO algorithm as the baseline, the accuracy and efficiency of the NN-based approach to parameter extraction are comprehensively studied and compared. Based on the parameter extraction results and the computational costs, the emerging NN-based methods are considered to be more effective for applications in test data-intensive scenarios.