Similarity search of DNA sequences is a fundamental problem in the bioinformatics, serving as the basis for many other problems. In this, the calculation of the similarity value between sequences is the most important, with the Edit distance (ED) commonly used due to its high accuracy, but slow speed. With the advantage of transforming the original DNA sequences into numerical vector form that retaining unique features based on properties. The calculation processing on these transformed data will be much faster, many times faster than a direct comparison on the original sequence. Additionally, from a long DNA sequence, after transformation, it typically has a lower storage capacity, making it have good data compression. The challenge of this job is to develop algorithms based on features that maintain biological significance while ensuring search accuracy, which is also the problem to be solved. Previous methods often used pure mathematical statistics such as frequency statistics and matrix transformations to construct features. In this paper, an improved algorithm is proposed based on both biological significances and mathematical statistics to transforming gene data into numerical vectors for ease of storage and to improve accuracy in similarity search between DNA sequences. Based on the experimental results, the new algorithm improves the accuracy of similarity calculations while maintaining good performance.