Because the uncertain embedding model of large-scale Knowledge Graph （KG） can not perform approximate reasoning on multiple logical relationships， a multi-relation approximate reasoning model based on Uncertain KG Embedding （UKGE） named UDConEx （Uncertainty DistMult （Distance Multiplicative） and complex Convolution Embedding） was proposed. Firstly， the UDConEx combined the characteristics of DistMult and ComplEx （Complex Embedding）， enabling it to infer symmetric and asymmetric relationships. Subsequently， Convolutional Neural Network（CNN） was employed by the UDConEx to capture the interactive information in the uncertain KG， thereby enabling it to reason inverse and transitive relationships. Lastly， the neural network was employed to carry out confidence learning of uncertain KG information， enabling the UDConEx to perform approximate reasoning within the UKGE space. The experimental results on three public data sets of CN15k， NL27k， and PPI5k show that， compared with MUKGE （Multiplex UKGE） model， the Mean Absolute Error （MAE） of confidence prediction is reduced by 6.3%， 30.1% and 44.9% for CN15k， NL27k and PPI5k respectively； in the task of relation fact ranking， the linear-based Normalized Discounted Cumulative Gain （NDCG） is improved by 5.8% and 2.6% for CN15k and NL27k respectively； in the multi-relation approximate reasoning task， it is verified that the UDConEx has the approximate reasoning ability of multiple logical relationships. The inability of traditional embedding models to predict confidence is compensated for by the UDConEx， which achieves approximate reasoning for multiple logical relationships and offers enhanced accuracy and interpretability in uncertainty KG reasoning.