In order to solve the problems that the segmentation accuracy and speed are difficult to balance and the algorithm cannot effectively distinguish similar foreground and background objects in the task of semi-supervised video object segmentation， a semi-supervised video object segmentation algorithm was proposed on the basis of deep and shallow feature fusion. Firstly， a pre-generated rough mask was used to process image features， thereby achieving more robust features. Secondly， deep semantic information was extracted by the attention model. Finally， deep semantic information and shallow position information were fused to obtain more accurate segmentation results. Experiments were conducted on multiple popular datasets. The experiment results demonstrate that the proposed algorithm improves the Jaccard （J） index by 1.8 percentage points and improves the comprehensive evaluation index mean of J and F?score J&F by 2.3 percentage points compared with Learning Fast and Robust Target Models for Video Object Segmentation （FRTM） algorithm on DAVIS 2016 dataset. Meanwhile， on DAVIS 2017 dataset， the proposed algorithm improves J index by 1.2 percentage points and improves the comprehensive evaluation index J&F by 1.1 percentage points compared with FRTM algorithm. The above results fully prove that the proposed algorithm can achieve higher segmentation accuracy with fast speed， and effectively distinguish background and foreground objects with strong robustness. It can be seen that the proposed algorithm has superior performance in balancing speed and accuracy and effectively distinguishing foreground and background.