The task of camouflaged object detection involves locating and identifying camouflaged objects in complex scenes. While deep neural network-based methods have been applied to this task, many of them struggle to fully utilize multi-level features of the target for extracting rich semantic information in complex scenes with interference, often relying solely on fixed-size features to identify camouflaged objects. To address this challenge, this study proposes a camouflaged object detection network based on multi-scale and neighbor-level feature fusion. This network comprises two innovative designs: the multi-scale feature perception module and the two-stage neighbor-level interaction module. The former aims to capture rich local-global contrast information in complex scenes by combining multi-scale features. The latter integrates features from adjacent layers to exploit cross-layer correlations and transfer valuable contextual information from the encoder to the decoder network. The proposed method has been evaluated on three public datasets: CHAMELEON, CAMO-Test, and COD10K-Test, and compared with the current mainstream methods. The experimental results demonstrate that the proposed method outperforms the current mainstream methods, achieving excellent performance across all metrics.