There are problems such as uneven point cloud density distribution and limited separability of single-point reflectance arise in the task of semantic segmentation of outcrop point clouds. To achieve efficient and accurate lithology segmentation of outcrop point clouds, this research proposes a lithology segmentation method with efficient channel attention mechanism (ECA) based on the multiple eigenvalues of outcrop voxel (MGECA). First, this method voxelizes the raw point cloud and computes the spatial-spectral feature parameters of each voxel. Then, a multi-granularity convolutional neural network is used for multi-scale feature fusion. Next, the classical self-attention mechanism in the Transformer model is improved using an ECA, allowing the weighted encoding of feature maps so the model can establish global spatial and spectral correlations. Finally, designs a dual-channel group convolution to connect the convolutional neural network and ECA, and achieve spatial and spectral feature integration, while reduce computational complexity. Experimental results show that MGECA achieved a lithology recognition total accuracy of 90.6% and a mean intersection over union of 70.4% on the Crescent Bay laser outcrop point cloud dataset, representing improvements of 31.7 percentage points and 24.7 percentage points, respectively, compared to DGPoint model. Results indicate that the proposed method has a significant advantage in segmentation performance within outcrop point cloud scenarios compared to existing methods.