The traditional security extraction technology of big data concealment features ignores the public key and key encapsulation of big data ciphertext, and the category of big data concealment features is chaotic, leading to the low extraction accuracy and high redundancy of this technology. Therefore, this paper proposes a security extraction method of big data concealment features based on mixed cipher system. Big data ciphertext is generated by public key encapsulation and key encapsulation mechanism in mixed cryptography. Symmetric encryption method and asymmetric encryption method are designed according to the ciphertext content, based on the hidden characteristics of the classification. The hidden feature phase space of big data is constructed by using the hidden features of different classes, and the correlation dimension value among big data is calculated to realize the safe extraction of the hidden features of big data. The experimental results show that, compared with the traditional methods, the proposed big data hidden feature extraction method has low redundancy, the average correct classification rate of big data hidden features is up to 95%, and the error of feature safe extraction is low, which verifies that the proposed method has better application performance.