Over the past two decades, deep learning technology has propelled machine natural language processing capabilities to rival or even surpass human levels in many tasks. Machine learning does not directly utilize the outcomes of human linguistic research (knowledge), but rather from human language materials (data). This situation should garner significant attention from linguists. As large language models, driven purely by data and computational power, have nearly constructed a modern Tower of Babel, the question of how to realize the value of linguistic knowledge through in-depth exploration of specific and subtle language phenomena looms large over every linguistic researcher. This paper proposes a research approach that generates text data from linguistic knowledge for evaluating machine understanding of spatial semantics. Over the past four years, we have organized four consecutive competitions on Chinese Spatial Cognition Evaluation (SpaCE): from SpaCE2021 to SpaCE2024, including 6 sub-tasks: Determination of Spatial Information Validity, Detection of Spatial Anomalies, Recovery of Spatial References, Identification of Spatial Semantic Roles, Recognition of Spatial Equivalences, and Spatial Position Reasoning. This paper introduces the design philosophy, dataset creation process, dataset overview, and the performance characteristics of machines in SpaCE tasks. Overall, large language models participating in the SpaCE competitions perform relatively well on tasks that rely on surface distribution features, that is, tasks with formal cues, but poorly on tasks that depend on deep semantic understanding, that is, tasks requiring cognitive abilities. In the current era of rapid AI development, where linguistic knowledge is passively marginalized in the field of natural language processing, the value of linguistic knowledge needs to be redefined. It should be used to guide the production of small, high-quality language data to enhance the effectiveness and efficiency of machine learning. For computational applications, grammatical research should pursue more challenging goals—adequate generation—beyond the objectives of adequate observation, description, and explanation.