Grey sequence generation can draw out and develop implied rules of the original data. Different kinds of generation methods were summarized and classified into two types: partial generation and whole generation. The average generation and stepwise ratio generation is disussed , the preference generation is regard as a special case of proportional division based on analysis geometric theory, propose an idea of using concave and convex status of discrete data to determine the generation coefficient. Based on the stepwise and smooth ratio generation, a tendency average generation is proposed and have a comparison using the data provided in papers listed in the references. The comparison proves that the new generation is better than the other two generations and errors are obviously reduced. Gray sequence generation can draw out and develop implied rules of the original data. Different kinds of generation methods were summarized and classified into two types: partial generation and whole generation. The average generation and stepwise ratio generation is disussed, the preference generation is regard as based on the stepwise and smooth ratio generation, a tendency average generation is proposed and have a comparison using the data provided in papers listed in the references. The comparison proves that the new generation is better than the other two generations and errors are obviously reduced.