Remote sensing objects have large scale differences. In order to solve the problems that they are prone to lead to difficulties in fine granularity multi-scale feature extraction and weak prediction part of effective representation under complex background interference， a multi-scale remote sensing object detection method （MFC） for multivariate feature extraction and characterization optimization based on the idea of anchor-free is proposed. In the feature extraction part， a multivariate feature extraction module （MFE） is designed to mine multi-scale features at the fine granularity level， expand the receptive field through grouping operation and cross group connection， enhance the combination effect of multiple feature scales， and further strengthen the focus on small objects by combining context information； The deep and shallow features are fully integrated by the deep layer aggregation structure to obtain a more comprehensive feature expression. In the prediction part， a characterization optimization strategy （COS） is proposed， which uses elliptical mapping to optimize tags to adapt to remote sensing targets with large aspect ratio. And a Coordinate-Pixel attention is designed to focus on multi-scale object channels， positions and pixel information， reduce complex background interference， and make effective information prominent. Ablation and contrast experiments were conducted on DIOR， HRRSD and RSOD datasets. The experimental results showed that the mAP of MFC model reached 70.9%， 90.2% and 96.9% respectively， which was superior to most existing methods. It effectively improved the problems of false detection and missing detection， and had strong adaptability and robustness.