To address the issues of insufficient feature extraction and inadequate context modeling in monocular depth estimation， we proposed a multi-scale feature fused monocular depth estimation optimization framework， FCMdepth， to enhance prediction performance. FCMdepth adopted an Encoder-Decoder （ED） structure， where the encoder， FC-Net， consists of MobileNetV3-F and CDBlock， and optimized features through multi-scale features and dilated convolutions， while the decoder， LapMA-Net， combined the Laplacian pyramid with an Efficient Multi-scale Attention （EMA） module to enhance cross-scale feature fusion and output accurate depth maps. Experimental results on the KITTI datasets show that， compared to Lapdepth， FCMdepth achieves lower values for the three error metrics： Root Mean Square Error （RMSE）， Root Mean Square Logarithmic Error （Log_RMSE），and Square Relative error （Sq_Rel）， with reductions of 0.831， 0.009， and 0.145， respectively， and improves three accuracy metrics by of 0.4， 0.8， and 0.3 percentage points， respectively. It can be seen that FCMdepth has superior performance compared to other models in most metrics and provides an effective reference for monocular depth estimation and 3D reconstruction in complex scenes.