I have a dataset containing a categorical feature with a missing rate 95%. What value can replace the missing cells? Or drop this feature?
You can turn it into a one-hot encoded feature with an added class of 'Missing', depending on the cardinality (how many categories are there). If the cardinality is too high, you will need to use other techniques for high cardinality features but you can still have 'Missing' as an additional category.
I have read the comments of another answer and seems like you have lots of missing data. I would then in this case recommend the mice imputation (multiple imputation with chained equation). It deals with all type of different variable types (numerical, categorical, binary) and fill the NA values depends on the type of the variable. If you use R, you can check https://cran.r-project.org/web/packages/mice/mice.pdf. It contains detailed package and function information and examples.