To solve the problem of internal covariate migration for deep auto encoder（ DAE）

a deep self-adaptive balance auto encoder（ DSBAE） is proposed for handwritten digital classification（ HDC）. Beside constructing the DSBAE network classification model

correcting method of the self-adaptive balance layer to the internal covariate migration is also focused on. Updating strategy of self-adaptive parameters is worked out according to principle of balanced network parameter system. The experiment compares DSBAE and other classification algorithms of deep learning in MNIST

USPS and PENDIGITS datasets

which proves that DSBAE can effectively solve the problem of internal covariate migration in deep network and obtain a clear advantage in accuracy of HDC.