A neural-architecture search algorithm based on a voting scheme was proposed to address the difference between network architectures that are automatically searched by existing algorithms and those that were evaluated by the algorithm. First， to solve the problem that uniform sampling ignores the importance of each network architecture， the training losses tested on small batch training data were used as performance estimators to sample candidate networks， thus concentrating computing resources on high-performance candidate network architectures. Second， a group sparsity regularization strategy was adopted to rank all candidate operations to solve the problem of selecting candidate operations in each node. This strategy could screen suitable candidate operations and further enhance the precision of path selection in the cell structure. Finally， by integrating the differentiable architecture search， noise and sparse regularization strategies， the optimal cell structure was selected using a weighted voting scheme， and the network architecture for 3D model recognition and classification was constructed. Experimental results indicate that the classification accuracy of the constructed network for 3D models reaches 93.9% on the ModelNet40 dataset， which is higher than that of current mainstream algorithms. The proposed algorithm effectively narrows the gap between the network architecture during the search and evaluation phases， thereby resolving the problem of inefficient network training caused by uniform sampling in previous neural-architecture search methods.