  Accurate photometric redshift (photo-$z$) estimation requires support from
multi-band observational data. However, in the actual process of astronomical
observations and data processing, some sources may have missing observational
data in certain bands for various reasons. This could greatly affect the
accuracy and reliability of photo-$z$ estimation for these sources, and even
render some estimation methods unusable. The same situation may exist for the
upcoming Chinese Space Station Telescope (CSST). In this study, we employ a
deep learning method called Generative Adversarial Imputation Networks (GAIN)
to impute the missing photometric data in CSST, aiming to reduce the impact of
data missing on photo-$z$ estimation and improve estimation accuracy. Our
results demonstrate that using the GAIN technique can effectively fill in the
missing photometric data in CSST. Particularly, when the data missing rate is
below 30\%, the imputation of photometric data exhibits high accuracy, with
higher accuracy in the $g$, $r$, $i$, $z$, and $y$ bands compared to the $NUV$
and $u$ bands. After filling in the missing values, the quality of photo-$z$
estimation obtained by the widely used Easy and Accurate Zphot from Yale (EAZY)
software is notably enhanced. Evaluation metrics for assessing the quality of
photo-$z$ estimation, including the catastrophic outlier fraction ($f_{out}$),
the normalized median absolute deviation ($\rm {\sigma_{NMAD}}$), and the bias
of photometric redshift ($bias$), all show some degree of improvement. Our
research will help maximize the utilization of observational data and provide a
new method for handling sample missing values for applications that require
complete photometry data to produce results.
