The Likelihood Ascend Search(LAS) algorithm is a heuristic neighborhood search algorithm that detects the received signals of large–scale Multiple–Input–Multiple–Output(MIMO) systems with space–division multiplexing. A Constellation Constraint–LAS(CC–LAS) is proposed for reducing the computational complexity of the traditional LAS algorithm. The algorithm first introduces a novel CC structure to determine the reliability of each candidate solution. Then, according to the reliability determination result, the neighborhood space of the candidate solution is narrowed. Finally, the unreliable candidate solution is detected by using the LAS algorithm. The proposed CC–LAS algorithm eliminates the inefficient processing of low–reliability signals by ignoring a large number of unnecessary neighbor vectors in the LAS neighborhood space. Hence, CC–LAS algorithm is capable of greatly reducing the computational complexity of the traditional LAS algorithm. The simulation results show that the BER performance of the proposed CC–LAS algorithm is very close to that of the traditional LAS algorithm; nevertheless, the computational complexity can be greatly reduced under the same Signal–to–Noise Ratio(SNR) compared to traditional LAS algorithm.