<p>Ancient buildings face the risk of damage due to natural disasters and human destruction, and digital protection technology becomes a key research direction. However, traditional image feature extraction and stitching methods have significant matching errors and unsatisfactory stitching effects when dealing with complex textures, lighting changes, and perspective distortions in ancient buildings. Therefore, a feature extraction method based on an improved scale invariant feature transformation algorithm is proposed, which introduces slope consistency constraints, K-nearest neighbor matching optimization, and random sampling consistency global optimization to improve matching accuracy. Combined with an improved discrete wavelet transform image fusion scheme, the stitching effect is optimized through visual saliency detection. The experiment outcomes indicate that the matching accuracy of the improved scale invariant feature transformation algorithm reaches 91.53%, and the false matching rate is reduced to 8.51%, which is better than the 90.16% of fast degree of freedom keypoint detection and the 92.00% of bidirectional scale invariant feature transformation. The improved discrete wavelet transform has a high structural similarity index of 0.97 in various scenarios, with a mean square error as low as 50.1, which is superior to traditional methods. The research improves the accuracy and visual authenticity of digital modeling of ancient buildings through technological optimization, providing efficient and low-cost digital means for cultural heritage protection.</p>