In recent years, contrastive learning has emerged as a powerful self-supervised approach for training large models. A key challenge in achieving high-performance contrastive models is the effective construction of high-quality positive sample pairs. Existing methods typically rely on RandomCrop of the same sample, often overlooking the semantic content and fine-grained details of the image that could be beneficial for model training. In this paper, we introduce a novel FilteringCrop technique to improve positive sample pair construction. We begin by developing a target rough localization algorithm, leveraging prior knowledge of the image’s semantic content, to facilitate coarse localization through image filtering prior to training. Next, we present a cropping algorithm designed to generate positive pairs that aid in more efficient model training. Additionally, we propose the Target Center Momentum Optimization algorithm to continuously refine the cropping center throughout the contrastive learning process. Our method demonstrates a 0.4% to 2.2% improvement in linear classification accuracy for SimCLR, MoCo V1&V2, and SimSiam on CIFAR-10, CIFAR-100, and Tiny-ImageNet. Furthermore, its effectiveness extends to downstream tasks such as object detection, and instance segmentation after pretraining on ImageNet 1k. Source code link: https://github.com/PPChuan/FilteringCrop .