This study proposes a data mining-based series important point-local outlier factor (SIP-LOF) algorithm to detect anomalous data from crustal deformation instruments. The primary objectives of this proposal are to improve the data availability rate of instruments and the efficiency of preliminary fault diagnosis by maintenance personnel. The initial observation sequence of deformation instruments is then separated into sub-sequences. The outlier distance and LOF of each point in the sequence are calculated, and the data point is identified as an outlier. This process enables the quantification of the anomaly degree of each data point. This approach facilitates the identification of anomalous events in precursor deformation observations, encompassing natural disturbances, equipment failures, and seismic precursors. The findings of the research demonstrate that, in comparison with conventional methods, the proposed method exhibits superior detection performance for precursor data from multiple stations, with a wider coverage of anomaly types. When the LOF threshold is set to 2.5, the average anomaly identification accuracy reaches its peak, which is of significant value for precursor data processing.