Identifying brain nodes with different responses under different conditions plays an important role in human brain research. Due to the low detection accuracy of existing single-voxel models and the excessive calculation time and usage limitations of the Low-rank Multivariate General Linear Model (LRMGLM), a brain node identification method based on Extended LRMGLM (ELRMGLM) was proposed. Firstly, an ELRMGLM that can simultaneously process all node data in two experiments was established to improve the accuracy of the algorithm with more time and space information. Then, an optimization function with spatio-temporal smoothing penalty terms was used to introduce the prior information and the model parameters were solved through the iterative algorithm. Finally, a quick selection strategy based on K-means clustering was adopted to speed up penalty parameter selection and brain node identification. In three sample experiments, the accuracy of ELRMGLM was respectively increased by about 20%, 8% and 20% compared with that of canonical Hemodynamic Response Function (HRF) method (canonical), Smooth Finite Impulse Response (SFIR) and Tikhonov-regularization and Generalized-Cross-Validation (Tik-GCV), which was slightly better than LRMGLM. However, the calculation time of ELRMGLM was 1/750 of that of LRMGLM. The experimental results show that ELRMGLM can effectively improve the identification accuracy and reduce the calculation time.