Aiming at Neural network Architecture Search （NAS） tasks， an NAS guided by Hybrid Heuristic Information （GHHI-NAS） algorithm was proposed. Firstly， a heuristic information construction module integrating prior knowledge and local search feedback was designed to generate multi-dimensional dynamic heuristic indicators， and a hybrid update strategy was combined to guide architecture search， thereby solving the problems of global exploration deficiency and local optimal traps caused by unidirectional updates in traditional NAS effectively. Secondly， a Covariance Matrix Adaptation Evolution Strategy （CMA-ES） update framework was adopted， enhanced by a hybrid fitness evaluation function， so that the algorithm was able to escape from small-model traps during early stage. Finally， a fitness sharing strategy was implemented to realize smooth evaluation of noise and improve population diversity. Additionally， a Monte Carlo swap sampling method with penalty mechanism was introduced to further reduce performance degradation caused by sampling. Experimental results demonstrate that GHHI-NAS has the validation accuracies of 97.55% and 83.44% on the CIFAR-10 and CIFAR-100 datasets， respectively， along with a test error rate of 24.7% on the ImageNet dataset and outstanding performance on the NAS-Bench-201 dataset， which is similar to or slightly surpasses those of Evolutionary NAS （ENAS） algorithm while requiring only 0.12 GPU-Days for search time， achieving low search costs and high test performance.