Learning from human skills has become one of the popular inspirations in grasp prediction and evaluation, but lack of effective methods on groups of grasp points for multi-fingered dexterous hands yields an open challenge. When facing an object, humans firstly predict a variety of options for grasps, which can be concerned as a complex multi-valued problem. After prediction, humans evaluate grasps and then choose the optimal one. Inspired by human skills, we propose Grasp Prediction Networks (GPNs) based on Convolutional Neural Networks (CNNs) and Mixture Density Networks (MDNs). The proposed GPNs map from a depth image to a set of parameters for Gaussian Mixture Model (GMM), from which candidate groups of grasp points can be sampled for prediction. Besides, we also propose Grasp Evaluation Networks (GENs) to evaluate candidate groups and then choose the optimal group of grasp points. The proposed GENs consider force-closure metric as grasp quality for evaluation. Different from other related work, our method (1) utilizes a probabilistic model to predict multiple groups of grasp points from a monocular depth image and (2) evaluates grasp quality with force-closure metric given a monocular depth image and a group of grasp points. Furthermore, we built a grasp dataset which consists of depth images, groups of grasp points and each group’s grasp quality. Herein, three different experiments were designed to validate our approach. The first one was a comparative experiment and revealed that GPNs show equivalent performance as GraspIt! in terms of high-quality grasp planning. The second one was also a comparative experiment, which validated that GENs can evaluate grasps as precisely as GraspIt!. Moreover, the last one was an actual experiment implemented on Shadow Hand Lite, and experimental results indicated that our approach achieved finely grasp of novel objects.