The design of convolutional neural network architecture is critical for achieving high characterization and prediction performance . However, manual design can be time-consuming and computationally expensive. Automatic architecture search methods are thus becoming popular. Presently, partial channel connections for memory-efficient differentiable architecture search being efficient in terms of search speed, memory utilization, and classification performance. However, the random sampling strategy can cause the loss of important information. To address these issues, this paper proposes differentiable neural architecture search with channel performance measurement, which extracts importance coefficients of channels with attention mechanism and ranks the channels accordingly to select the most important ones. The proposed algorithm also includes a temperature regularization coefficient to improve the transformation from discrete to continuous architecture weight and reduce discretization errors. Experimental results show that the proposed algorithm achieves good performance while significantly reducing computational resources.