## 1 Introduction  
Cross-country skiing (XCS) is a demanding sport that involves both training and competition across diverse terrains [1]. Skiers must adapt by shifting between sub-techniques in freestyle (skating) and classical styles, primarily based on the terrain type [2], in an effort to achieve the highest possible speed given a fixed race distance [3]. Previous studies have explored the factors that influence performance, both in sprint and distance XCS races. In sprint XCS, key factors such as cycle characteristics, oxygen uptake, body composition, and performance in different race sections have been shown to influence overall performance. For instance, cycle length has been identified as a major determinant of skiing speed and is also related to skiing economy [4]. Cycle rate was associated with maximal speed capacity, and self-selected cycle rates may improve economy at sub-maximal speeds [4]. Furthermore, high cycle rates are crucial for sprint performance under fatigue [5]. Sprint specialists were found to have superior anaerobic capacity and peak oxygen uptake compared to distance skiers [6], and the sprint performance correlatesstrongly with maximal oxygen consumption (VO\({}_{2}\)max) [7]. Skiers with a higher percentage of lean mass relative to fat mass also tend to achieve greater peak speed [8]. In addition, performance on both uphill and flat terrain has been reported to correlate with overall sprint race performance [9]. In distance XCS, key factors such as physiological endurance, the ability to manage fatigue over extended periods, and strategic pacing are critical. Studies have shown that aerobic capacity is crucial for distance events, with faster skiers typically exhibiting higher VO\({}_{2}\)max values [5]. Studies also reported that the distance XCS performance is highly related to maximal oxygen uptake [10], and it is an indicator of performance in both sprint and distance events [11]. It is important to manage lactate accumulation and muscular fatigue [7], and it is essential for maintaining a steady pace over longer periods [12]. Strategic pacing and endurance, especially during uphill terrains and final race stages, are vital [13]. Although numerous studies have investigated the impact of various biomechanical and physiological parameters on skiing performance separately, integrating both aspects is crucial for high-performance skiing [13]. Traditional research predominantly relies on linear models to explore the relationship between skiing speed and various factors. For example, VO\({}_{2}\)max in running tends to be strongly correlated with skiing performance [12], and factors like speeds at blood lactate concentrations and peak aerobic power are known to influence speed [14]. These approaches may fail to capture the non-linear dynamics that govern athlete performance in XCS [15]. Neural networks have the capability to discern intricate patterns from large, multidimensional datasets, making them particularly well suited to predicting performance. Recent studies used machine learning methods to predict techniques [16], analyze sub-technique and cycle characteristics [17], and even explore motion recognition using optical sensors [18]. These studies demonstrate the potential of sensor data and neural networks to provide real-time feedback, supporting a comprehensive, data-driven approach to performance analysis. However, previous studies utilizing machine learning [19; 20] to predict performance often overlooked key biomechanical [21; 22; 23; 24], physiological [10; 12], or the body composition parameters [8]. A comprehensive approach that integrates both physiological and biomechanical factors is needed to create more accurate predictions of skiing speed. Consequently, the primary aim of this study is to identify the key biomechanical and physiological factors that would have the greatest impact on skiing performance among Chinese elite athletes. Based on this information, a neural network model would be further developed and assessed for predicting XCS performance.