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Aiming at the problem that the existing face recognition algorithms have low recognition rates and poor robustness in real and complex environments such as motion blur and low light, which makes it difficult to be stably applied to actual face recognition tasks, a face recognition method in complex environments based on adaptive feature extraction network is proposed. The network combines the feature extraction technology of traditional methods with the feature representation ability of deep learning network, and realizes the task of stable face recognition in different complex environments. An adaptive texture feature extraction algorithm is designed, which realizes feature extraction by automatically obtaining the threshold value and improves the network computing efficiency. The back propagation algorithm is used to improve the deep belief network, and the conjugate gradient algorithm is introduced to solve the gradient disappearance problem of the network, which reduces its convergence time and improves the algorithm's robustness. The experimental results show that the accuracy of the proposed method reaches 99.72%, 89.54% and 88.75% respectively on the standard LWF dataset and the complex environment CASIA and MS1M datasets. The number of parameters and network calculations are 2.84M and 0.67G respectively, which are superior to the comparison algorithm and can meet the needs of face recognition tasks in complex environments.