Time series segmentation is an important research direction in the field of data mining. At present, the time series segmentation technique based on matrix profile (MP) has received increasing attention from researchers and has achieved great research results. However, this technique and its derivative algorithms also have their own short comings. For one thing, the matching of similar subsequences in the case of arcs crossing non-target activity states arises when the fast low-cost semantic segmentation algorithm based on MP is employed for time series segmentation of a given activity state and the nearest neighbors are connected by arcs. For another, the existing segmentation point extraction algorithm uses a given length window when extracting segmentation points. In this case, the segmentation points obtained are highly likely to exhibit large deviations from the real values, which reduces the accuracy. To address the above problems, this study proposes a time series segmentation algorithm limiting the arc cross, namely limit arc curve cross-FLOSS (LAC-FLOSS). This algorithm adds weights to arcs to obtain a kind of weighted arcs and solves the subsequence mismatch problem caused by the state crossing of the arcs by setting a matching distance threshold. In addition, an improved segmentation point extraction algorithm, namely, the improved extract regimes (IER) algorithm, is proposed. This algorithm extracts the extremes from the troughs according to the shape properties of the sequence of corrected arc crossings (CAC), thereby avoiding the problem that segmentation points are obtained at non-inflection points when the windows are used directly. Comparative experiments are conducted on the public datasets datasets_seg and MobiAct, and the results verify the feasibility and effectiveness of the above two solutions.