Accurate identification of DNA/RNA modification sites is imperative for the study of their biological functions. Machine learning algorithms are unable to utilize sequence data directly to construct models for predicting these sites. Consequently, the development of DNA/RNA sequence feature representation algorithms is paramount for the effective encoding of sequence data into a usable numerical format, thereby facilitating the construction of high-performance machine learning predictive models. Existing DNA/RNA sequence feature representation algorithms suffer from the problems of simple extracting information, failure to take into account the sequence position and order information, and irrelevant or redundant features brought about by multi-methods together. This leads to the inability of machine learning models to break through the bottleneck of prediction performance. To address the aforementioned issues, this paper introduces the BiPSDP (Bidirectional Position-Specific Dinucleotide Propensities) algorithm. This algorithm extracts dinucleotide position-specific propensities from both forward and backward sequence directions. The incorporation of a parameter for dinucleotide spacing is pivotal in capturing global order information, with DNA/RNA sequences being encoded as numerical features that are rich in class-distinguishing information. The validity of the BiPSDP algorithm was tested by constructing a DNA/RNA modification site prediction model using the SVM learning machines, and a comparison was made with seven existing representation algorithms across seven modification types. The experimental results demonstrate that the prediction model with BiPSDP consistently outperforms the comparison models, thus validating its value as a tool for constructing prediction models for various DNA/RNA modification sites. The code of BiPSDP is available at https://github.com/Mingzhao2017/BiPSDP .