To solve the problems of weak texture and reflection in the local magnification observation of jewelry， mineral， and metal samples， a multiview stereo three-dimensional （3D） reconstruction algorithm based on feature extraction is proposed. The lens of the microscope is fixed at a fixed angle， and the image sequence is obtained via a multi-angle imaging of the 3D specimen by moving the carrier platform. By combining the advantages of the Harris and SIFT algorithms， the SIFT algorithm in the original SFM reconstruction is improved to the Harris-SIFT algorithm for feature extraction and matching， which improves the performance of feature information extraction in weak texture regions of microscopic images. By using the full convolution neural network combined with the depth residual network to estimate and predict the depth of the input image， the predicted depth information is combined with the MVS depth map through the threshold method， the MVS depth map is modified， the dense point cloud of the object is reconstructed， and more point clouds are reconstructed with structural integrity. Experiments performed using a VHX-6000 digital microscope system show that the number of point clouds reconstructed using this algorithm is 31.25% higher than that reconstructed by the original MVS reconstruction algorithm， and the overall reconstruction time is reduced by 21.16%.