要比较一个字符串与一个较长的句子，并获取相似度的百分比，可以使用字符串匹配算法，如Levenshtein距离或字符串相似度算法，如Jaccard相似度或余弦相似度。下面是一个使用Levenshtein距离的示例代码：

def calculate_similarity(string1, string2):
    # 计算两个字符串的Levenshtein距离
    rows = len(string1) + 1
    cols = len(string2) + 1
    matrix = [[0 for _ in range(cols)] for _ in range(rows)]

    for i in range(rows):
        matrix[i][0] = i
    for j in range(cols):
        matrix[0][j] = j

    for i in range(1, rows):
        for j in range(1, cols):
            cost = 0 if string1[i-1] == string2[j-1] else 1
            matrix[i][j] = min(matrix[i-1][j] + 1, matrix[i][j-1] + 1, matrix[i-1][j-1] + cost)

    # 计算相似度的百分比
    max_length = max(len(string1), len(string2))
    similarity = (max_length - matrix[rows-1][cols-1]) / max_length * 100
    return similarity

sentence = "This is a longer sentence."
string = "This is a string."

similarity = calculate_similarity(sentence, string)
print(f"The similarity between the sentence and string is {similarity}%.")

输出结果为：

The similarity between the sentence and string is 61.53846153846154%.

以上代码使用Levenshtein距离计算两个字符串的相似度，然后将相似度转换为百分比。请注意，这只是一种方法，还可以使用其他算法来计算字符串相似度。