Over-segmentation leads to incorrect predictions and reduces segmentation quality in existing action segmentation algorithms. To address this， the reference from video action boundary information was independently introduced for each stage in the backbone， which was based on a multi-stage temporal convolutional network. To avoid the model solidification caused by the application of the same boundary information at all stages， a weight adjusting block composed of multilayer parallel convolution was proposed to adjust the boundary values involved in the output calculation of each stage and process various samples differently. The reference from the adjustable boundary information was used to smoothen the output of each stage according to the time sequence， significantly reducing the over-segmentation error. Experimental results show that the proposed method outperforms existing methods in the three video action segmentation datasets GTEA， 50Salads and Breakfast. Compared with the boundary-aware cascade networks（BCN） algorithm， the segmentation edit score is increased by 1.7% on average， and the reconciliation score between accuracy and recall rate is increased by 1.5% on average.