The accuracy of the direction of arrival (DOA) estimation algorithm and the resolution are limited by the number of channels. To address these issues, this article proposes a meshless DOA estimation algorithm based on channel compression-atomic norm minimization (CC-ANM). First, the algorithm compresses the number of channels. Then, the eigenvalue decomposition is performed on the covariance matrix of the compressed data. The decomposed eigenvalues and eigenvectors are used to construct a new observation vector to solve the ANM problem under the single snapshot model. Finally, the Toeplitz matrix is established according to the optimal solution of the positive SDP problem. The DOA parameter estimation result of the signal is achieved through its Vandermonde decomposition. Simulation experiments show that the CC-ANM algorithm can achieve an estimation accuracy below 0. 1° when the number of array elements is 20, the compression rate is 2, the SNR is 20 dB, and the number of snapshots is 200. The 100% measurement is possible for signals with an angular separation of more than 2°. The test data received by the instrument with an incident angle of 0° show that the estimation accuracy of the algorithm is below 0. 3°, which is better than the compressed sensing algorithm under the same condition.