Artificial immune system (AIS) is one of the important branches of artificial intelligence technology, and it is widely used in many fields such as anomaly detection, data mining, and machine learning. The detectors are its core knowledge set, and the application effects are determined by the generation, optimization, and detection of the detectors. At present, the problem space of AIS mainly applied real-valued shape-space. But the detectors in the real-valued shape-space have some problems that have not been solved, such as the holes in the non-self-shape-space, slow speed of generation, detector overlapping redundancy, dimension curse, which lead to the unsatisfactory detection effects. In view of this, based on the neighborhood shape-space, a new shape-space, and the improved neighborhood negative selection algorithm, a multi-source-inspired neighborhood negative selection algorithm (MSNNSA) is proposed by introducing chaotic map and genetic algorithm. And then, based on this algorithm, the multi-source-inspired immune detector generation and detection methods in neighborhood shape-space are built to make the construction and generation more targeted, so that the generated detectors have better distribution performance. Meanwhile, the method also improves the detectors' generation efficiency and the detection performances, and overcomes the shortcomings in the real-valued shape-space mentioned before. Experimental results show that the proposed method enhances generation efficiency, whole detection performances, and stability.