Horizontal well waterflooding is increasingly used in recent years because it can enlarge the control volume of injected water. However, compared with vertical well waterflooding, the injected water is easier to breakthrough, which increases the difficulty in controlling uniform flooding. Therefore, quantitative characterization and evaluation methods of the flow field are of significant importance for further development and adjustment. In this study, based on streamline simulation results, a program was developed to extract streamline positions and attribute information saved in keyword format, constructing indicators such as production potential coefficient, oil displacement efficiency, and water cut as flow field characterization indexs. By combining clustering algorithms and the Silhouette Coefficient (SC) algorithm, the Kmeans algorithm was selected as optimal, proposing a flow field evaluation and adjustment method based on streamline clustering. The results indicate that this method can identify ineffective water circulation channels and regions with development potential within the reservoir, providing a basis for subsequent flow field adjustment. According to the streamline clustering evaluation results, the X block of the E oilfield adjusted the flow field through optimized injection-production rate, effectively improving block development performance. The recovery rate increased from 25.9% to 28.1%, and the overall water cut decreased from 78.34% to 77.87%. This work provides an effective evaluation method for directly characterizing the underground flow field water flooding effect, which is of significant importance for adjusting horizontal well water injection development strategies.