The reconstruction of cerebral blood flow velocity (CBFV) is essential for the long-term assessment of cerebrovascular function. To this end, this study proposes a multivariate time-series model based on a Transformer encoder to reconstruct CBFV signals. The model utilizes time-series signals of arterial blood pressure (ABP) and CO2 to achieve accurate CBFV reconstruction. A long short-term memory (LSTM) module is introduced into the model to address the limitation of dispersed global attention in the attention mechanism, thereby enhancing the processing of local details. Additionally, a mixed loss function is employed to control local waveform errors, improving reconstruction accuracy. Furthermore, a transfer learning strategy is designed based on the correlation between ABP and electrocardiogram (ECG) signals to alleviate the impact of data scarcity on the reconstruction task. Experimental results on the cerebrovascular regulation dataset of diabetic patients from Beth Israel Deaconess Medical Center demonstrate that the proposed model outperforms existing regression models and deep learning models in CBFV reconstruction tasks. The results show a Pearson correlation coefficient of 0.518, a dynamic time warping distance of 17.879, and a mutual information value of 0.343 between the reconstructed and true values. Additionally, the model can reconstruct 200 data points within 0.04 seconds.