Camouflage object segmentation aims to segment objects that are similar to their surroundings. However, due to the inherent similarity between the foreground target and the background environment, it is difficult to fully extract the discriminative features and accurately locate the boundary of the camouflage objectly. To this end, a camouflaged object segmentation framework termed Multi-scale Feature Aggregation and Boundary Generation Networks (MFABGNet) is proposed. Specifically, we propose a multi-scale feature extraction encoder, which uses Transformer to extract global background features, and an efficient Feature Reconstruction Convolution module to extract and enhance local foreground features. In addition, we propose a hierarchical features aggregation decoder to facilitate aggregation of multi-scale features. The proposed decoder consists of two modules: Cascaded Feature Aggregation Module and Boundary Generation Module. The former concentrates on addressing the scale disparities among various features,aggregates global and local features, and efficiently facilitates the interaction between foreground and background information. The latter leverages spatial information from low-level features to progressively refine the boundary layer by layer, thereby producing a boundary representation that accurately identifies the camouflage object. The experimental results demonstrate that our model achieves competitive performance on the three benchmark datasets. Compared to other SOTA methods, the average maximum improvement of S-measure, E-measure, weighted F-measure and Mean Absolute error is up to 17.6 \(\%\) , 22.1 \(\%\) , 47.5 \(\%\) and 10.8 \(\%\) respectively. The codes are available at: https://github.com/jeremy0922/1.git .