Ultra-flexible electrodes are nanometer-thick polymer-based brain-computer interface devices implanted via a “sewing machine” paradigm: microneedles rapidly inject the electrode into the target brain region under visual guidance and immediately withdraw, leaving only the independently floating electrode for long-term neural recording. Visual positioning errors, fragile structure, and long-distance transport can easily cause abnormalities such as hooking failure, fracture, and detachment. However, existing implantation systems pay little attention to real-time automatic identification of abnormal events, which undermines the reliability of automated implantation. To solve the problem, a force-vision fusion-based method was proposed for abnormal events identification, which leveraged the complementary characteristics of high-frequency, low-signal-to-noise-ratio (SNR) force signals and low-frequency, high-SNR visual signals. Specifically, it first identified abnormal events in real time by evaluating the change rate of force and the absolute deviation of the force signal from its initial value. Then, it identified the type of abnormal events with a vision-language model, which was fine-tuned with the low-rank adaptation method and structured textual prompt. Finally, the operation was adjusted according to the identified event type. Experimental results show that the proposed method accurately identifies both the timing and type of abnormal events. It not only prevents unnecessary tissue damage caused by inserting a microneedle without an attached microelectrode, but also enables re-hooking and insertion after hooking failure, thereby avoiding microelectrode waste and shortening implantation time.