"Fix it" is not really the correct way to think about this.
Bootstrapping is a statistical method, used to get around the problem that you have imperfect data ( _i.e._ not as many sequences as you would like). Typically, the way it works is to generate a "fake" sequence based on the real sequences, add it to the alignment and see whether sequences still cluster on the same branch. You repeat that an arbitrary number of times (perhaps 100 or 1000) and count the proportion of times that you got the same branch.
Low values mean lower confidence. You may be able to improve the scores using better data, such as more real sequences or better optimization of the alignment. However, it's important to realise that these are statistical methods and they do not really give "right" or "wrong" answers. Rather, you should think of the statistic as telling you something about your data.