Differentiable ARchiTecture Search （DARTS） can design neural network architectures efficiently and automatically. However， there is a performance “wide gap” between the construction method of super network and the design of derivation strategy in it. To solve the above problem， a differentiable neural architecture search algorithm with constraint in optimal search space was proposed. Firstly， the training process of the super network was analyzed by using the architecture parameters associated with the candidate operations as the quantitative indicators， and it was found that the invalid candidate operation none occupied the architecture parameter with the maximum weight in deviation architecture， which caused that architectures obtained by the algorithm had poor performance. Aiming at this problem， an optimized search space was proposed. Then， the difference between the super network of DARTS and derivation architecture was analyzed， the architecture entropy was defined based on architecture parameters， and this architecture entropy was used as the constraint of the objective function of DARTS， so as to promote the super network to narrow the difference with the derivation strategy. Finally， experiments were conducted on CIFAR-10 dataset. The experimental results show that the searched architecture by the proposed algorithm achieved 97.17% classification accuracy in these experiments， better than the comparison algorithms in accuracy， parameter quantity and search time comprehensively. The proposed algorithm is effective and improves classification accuracy of searched architecture on CIFAR-10 dataset.