Multi-hop complex reasoning over incomplete knowledge graphs has been extensively studied, but research on numerical knowledge graphs remains relatively limited. Recent approaches focus on separately encoding entities and numerical values, using neural networks to process query encodings for reasoning. However, in complex multi-hop reasoning tasks, numerical values are not merely symbols; they carry specific semantics and logical relationships that must be accurately represented. % Directly encoding numerical values often leads to the loss of such semantic information. In this work, we propose a Complex Numerical Reasoning with Numerical Semantic Pre-Training Framework \textbf{(CNR-NST)}. % Specifically, we designed a joint link predictor to learn numerical semantics. The proposed framework is the first to enable binary operations on numerical attributes in numerical knowledge graphs, allowing new numerical attributes to be inferred from existing knowledge. The CNR-NST framework can perform binary operations on numerical attributes in numerical knowledge graphs, enabling it to infer new numerical attributes from existing knowledge. Our approach effectively handles up to 102 types of complex numerical reasoning queries. On three public datasets, CNR-NST demonstrates SOTA performance in complex numerical queries, achieving an average improvement of over 40% compared to existing methods. Notably, this work expands the range of query types for complex multi-hop numerical reasoning and introduces a new evaluation metric for numerical answers, which has been validated through comprehensive experiments.