In order to improve the detection accuracy of various types of defects in the electroluminescence imaging of solar cells， a solar cell surface defect detection algorithm CMFAnet was proposed by fusing multiscale features and attention mechanism. Firstly， for the characteristics of solar cell surface defects with large scale span， an enhanced multi-scale feature fusion method was designed， whose basic unit consists of a feature alignment module and a feature fusion module connected in series， and for the feature information with different semantic levels， the feature alignment module adjusts their scales， so that these features can be fused together more easily； secondly， for the characteristics of solar cell surface defects with high level and variable geometry， a deformable ghost convolution module is designed. Secondly， for the characteristics of high degree of similarity between defective features and background features on the solar cell surface and variable geometry， a deformable ghost convolution module was designed， whose basic unit consists of feasible variant convolution， multiplexed coordinate attention mechanism， and ghost convolution； the multiplexed coordinate attention mechanism optimizes the generation of offset in the feasible variant convolution， and the introduction of ghost convolution mechanism effectively reduces the FLOPs of the network model. The experimental results show that the mAP of this paper's method reaches 91.4% on the photovoltaic cell defect anomaly detection dataset PVEL-AD， which is improved to different degrees compared to other mainstream target detection networks.