In wireless sensor networks (WSNs), volatility of the link affects the accuracy of data transmission of the upper routing protocols. To improve the efficiency, link quality evaluation is used to avoid choosing poor links and increase the efficiency of routes. Link hierarchy grading in current link quality estimation study is subjective without unity. Aiming at this problem, we use the entropy method to calculate the weight of evaluation parameters to eliminate the interference of subjective factors in the calculation. Since the link quality is affected by multiple feature attributes, we then determine link quality grades by the closeness analysis method. According to the grades, we propose a link quality evaluation method based on closeness grades, which uses the dispersion degree of classes to establish a binary decision tree for classifying link quality. We also build a four level binary decision tree estimation model of link quality based on the support vector machine (SVM). Besides, we utilize a hybrid algorithm to optimize the parameters of the kernel function. Experimental results indicate that the improved algorithm can increase estimation accuracy  with less training time. Comparison in multinetwork scenarios shows that the proposed model outperforms the conventional link quality estimation model based on LQI and the estimation model based on BP neural network. It can accurately assess current link quality with a small number of probe packets, thus reducing energy consumption with good adaptive capacity to the environment.