Hemorrhage volume is an important indicator for the grading of intracerebral hemorrhage disease， the determination of treatment options， and the judgment of prognosis. However， because of the complexity of the brain structure and the variety of morphology and location of the hematoma， accurate and reliable segmentation of the hematoma and measurement of the amount of hemorrhage are extremely difficult. This paper presents an algorithm for cerebral hematoma segmentation and blood volume measurement based on a self-attention mechanism deep learning network. First， to overcome the complexity of brain structure and make up for the shortcomings that convolution module can only perform linear operations and extract local features， a self-attention module is introduced at the end of the encoder of the segmentation network， and through higher order operations， the feature association properties of the whole domain of the image are extracted and the hematoma is extracted from a global perspective. Second， a channel and spatial attention module is introduced to obtain weights on the individual channels and feature regions through training learning， by which useful information is highlighted and useless information is suppressed. Finally， the hemorrhage volume is calculated based on the hematoma segmentation results of multislice CT imaging slices in patients with intracerebral hemorrhage. The experimental results on the real CT imaging data set of intracerebral hemorrhage show that the proposed algorithm achieves better results on cerebral hematoma segmentation and hemorrhage volume measurement in multiple cases， and even is still relatively effective in the case of irregular shape or close to skull.