Is it accurate to say that "K-means clustering the vectors based on keywords weight similarity"?
K-means clusters points (in your case represented as 745 dimensional vectors) by their similarity, that is some distance measure between points (usually the Euclidean distance).
TF-IDF produces a vector from a sentence or document, where each entry (axis) represents the frequency of a word divided by the frequency of it's occurrence in all sentences or documents, hence the name. Other weighting scheme are possible as well, see here.
If two vectors are very close to each other, it means that the content of their documents is very similar. Therefore it's likely they end up in the same cluster. In contrast, if two vectors are far away, the words in each document might be completely different or the frequency of words might be different.
So the distance between vectors can be interpreted as measure of similarity between the documents content.